Project 2

library(tm)

## Warning: package 'tm' was built under R version 4.4.3

## Loading required package: NLP

## Warning: package 'NLP' was built under R version 4.4.2

library(stringr)

## Warning: package 'stringr' was built under R version 4.4.3

library(wordcloud)

## Warning: package 'wordcloud' was built under R version 4.4.3

## Loading required package: RColorBrewer

library(RColorBrewer)
library(topicmodels)

## Warning: package 'topicmodels' was built under R version 4.4.3

library(ldatuning)
library(tidytext)

## Warning: package 'tidytext' was built under R version 4.4.3

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(textdata)

## Warning: package 'textdata' was built under R version 4.4.3

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.3

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:NLP':
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##     annotate

library(tidyr)
library(stm)

## Warning: package 'stm' was built under R version 4.4.3

## stm v1.3.8 successfully loaded. See ?stm for help. 
##  Papers, resources, and other materials at structuraltopicmodel.com

library(tidystm)

Dataset description

The dataset contains 2000 reviews of different restaurants. Reviews describe customer experience while visiting the place. Metadata chosen for analysis contains restaurant name (for the purpose of analysis binary variable is created where certain restaurants are marked as ‘our’, representing businesses owned by the client), rating (negative if less than 3.5, positive if otherwise), time (only year is extracted). We would like to know which topics are associated with more negative ratings, how topics change over time and how client’s restaurants differ in terms of topics from the competitors.

data <- read.csv("reviews_rest.csv", header = TRUE, stringsAsFactors = FALSE)
head(data)

meta <- data.frame(
  rating = data$Rating,     
  time = data$Time,
  place = data$Restaurant
)

meta$time_dt <- as.POSIXct(meta$time, format = "%m/%d/%Y %H:%M")
meta$time_ym <- format(meta$time_dt, "%Y")
meta$time_ym <- as.numeric(meta$time_ym)
meta$rating_group <- ifelse(as.numeric(meta$rating) >= 3.5, "pos", "neg")
meta$rating_group <- factor(meta$rating_group)


target_places <- c(
  "Tiki Shack",
  "Mazzo - Marriott Executive Apartments",
  "Hyderabad Chefs",
  "Ulavacharu",
  "Tempteys",
  "10 Downing Street",
  "Karachi Cafe",
  "Kritunga Restaurant",
  "AB's - Absolute Barbecues",
  "Frio Bistro"
)

meta <- meta %>%
  mutate(our_place = as.integer(place %in% target_places))

processed <- textProcessor(
  documents = data$Review,
  metadata = meta,
  lowercase = TRUE,   
  removestopwords = TRUE,
  removenumbers = TRUE,
  removepunctuation = TRUE,
  wordLengths = c(3, Inf),
  stem=TRUE,
  language = "en"
)

## Building corpus... 
## Converting to Lower Case... 
## Removing punctuation... 
## Removing stopwords... 
## Removing numbers... 
## Stemming... 
## Creating Output...

prep <- prepDocuments(
  processed$documents,
  processed$vocab,
  processed$meta,
  lower.thresh = 2
)

## Removing 3697 of 5570 terms (4332 of 45477 tokens) due to frequency 
## Removing 5 Documents with No Words 
## Your corpus now has 1984 documents, 1873 terms and 41145 tokens.

docs  <- prep$documents
vocab <- prep$vocab
meta  <- prep$meta

plotRemoved(processed$documents, lower.thresh = seq(1, 25, by = 1))

rr1<-prep$docs.removed
rr1 <- as.numeric(rr1)

model_10 <- stm(docs, vocab, K = 10, data = meta, init.type = "Spectral")

## Beginning Spectral Initialization 
##   Calculating the gram matrix...
##   Finding anchor words...
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##   Recovering initialization...
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## Topic 1: good, servic, great, time, starter 
##  Topic 2: chicken, tast, biryani, item, quantiti 
##  Topic 3: place, nice, serv, tri, one 
##  Topic 4: restaur, dish, time, veg, tri 
##  Topic 5: food, visit, love, can, amaz 
##  Topic 6: friend, lot, best, dessert, averag 
##  Topic 7: best, order, even, qualiti, cake 
##  Topic 8: realli, ambienc, like, will, fri 
##  Topic 9: order, staff, buffet, disappoint, lunch 
##  Topic 10: good, price, overal, paneer, main 
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## Topic 1: good, servic, great, time, just 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, tri, one 
##  Topic 4: restaur, dish, tri, time, veg 
##  Topic 5: food, visit, love, can, amaz 
##  Topic 6: friend, best, lot, dessert, averag 
##  Topic 7: order, even, best, cake, chocol 
##  Topic 8: realli, ambienc, like, well, fri 
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## Topic 1: good, servic, great, time, also 
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##  Topic 3: place, nice, serv, tri, experi 
##  Topic 4: restaur, dish, tri, one, went 
##  Topic 5: food, visit, love, can, amaz 
##  Topic 6: friend, best, lot, dessert, qualiti 
##  Topic 7: even, order, take, chocol, best 
##  Topic 8: realli, ambienc, like, well, rice 
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## Topic 1: good, servic, great, time, also 
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##  Topic 6: friend, best, qualiti, dessert, lot 
##  Topic 7: even, order, take, chocol, cake 
##  Topic 8: realli, ambienc, like, well, rice 
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##  Topic 7: even, order, take, chocol, never 
##  Topic 8: realli, ambienc, like, well, rice 
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##  Topic 6: friend, best, qualiti, dessert, lot 
##  Topic 7: even, take, never, order, chocol 
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##  Topic 9: order, buffet, disappoint, lunch, dont 
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## Topic 1: good, servic, time, ambienc, great 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, experi, awesom 
##  Topic 4: tri, one, restaur, dish, like 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, lot, fish 
##  Topic 7: even, bad, take, never, chocol 
##  Topic 8: realli, well, like, rice, just 
##  Topic 9: order, disappoint, got, lunch, dont 
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## Topic 1: good, servic, ambienc, time, great 
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##  Topic 3: place, nice, serv, experi, awesom 
##  Topic 4: tri, one, restaur, dish, like 
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##  Topic 6: friend, best, qualiti, fish, lot 
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## Topic 1: good, servic, ambienc, great, time 
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##  Topic 6: friend, best, qualiti, fish, lot 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, restaur, like, dish 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, restaur, like, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, fish, lot 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, like, spici 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, restaur, like, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, fish, tri 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, like 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, like, restaur, dish 
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##  Topic 6: friend, best, qualiti, fish, tri 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, like, restaur, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, fish, tri 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, tri, like, restaur, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, tri, fish 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, tri, fish 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, tri, fish 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, amaz 
##  Topic 6: friend, best, qualiti, tri, fish 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: realli, well, rice, spici, fri 
##  Topic 9: order, time, disappoint, got, dont 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: friend, best, tri, qualiti, fish 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: friend, best, tri, fish, qualiti 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, serv, awesom, must 
##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: friend, best, fish, tri, qualiti 
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## Topic 1: good, servic, ambienc, great, also 
##  Topic 2: chicken, tast, biryani, quantiti, item 
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##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: friend, best, fish, tri, lunch 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 6: best, fish, tri, lunch, hyderabad 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: best, fish, tri, hyderabad, lunch 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: well, realli, spici, fri, chees 
##  Topic 9: order, time, food, disappoint, dont 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, friend 
##  Topic 6: best, fish, tri, hyderabad, tikka 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: well, realli, spici, fri, chees 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, friend 
##  Topic 6: best, fish, tri, hyderabad, tikka 
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##  Topic 8: well, realli, chees, fri, sauc 
##  Topic 9: order, time, food, disappoint, serv 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, friend 
##  Topic 6: best, fish, tri, tikka, hyderabad 
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##  Topic 8: well, realli, chees, fri, perfect 
##  Topic 9: order, time, food, disappoint, serv 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, friend 
##  Topic 6: best, fish, tri, tikka, hyderabad 
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##  Topic 8: well, realli, perfect, chees, fri 
##  Topic 9: order, time, food, disappoint, serv 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 4: one, like, tri, restaur, dish 
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##  Topic 6: best, fish, tri, tikka, hyderabad 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 8: well, realli, perfect, chees, sauc 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 4: one, like, tri, restaur, dish 
##  Topic 5: food, visit, love, staff, friend 
##  Topic 6: best, fish, tri, tikka, hyderabad 
##  Topic 7: even, bad, take, deliveri, never 
##  Topic 8: well, perfect, realli, chees, sauc 
##  Topic 9: order, time, food, serv, disappoint 
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## Topic 1: good, servic, ambienc, great, also 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, one, tri, restaur, dish 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, also 
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## Topic 1: good, servic, ambienc, great, food 
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## Topic 1: good, servic, ambienc, great, food 
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## Topic 1: good, servic, ambienc, great, food 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, one, restaur, food 
##  Topic 5: food, visit, staff, love, friend 
##  Topic 6: best, fish, tri, tikka, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
##  Topic 8: perfect, well, chees, realli, sauc 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, servic, ambienc, great, food 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, one, food 
##  Topic 5: food, visit, staff, friend, love 
##  Topic 6: best, fish, tri, tikka, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, servic, food, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, one, food 
##  Topic 5: food, visit, staff, friend, love 
##  Topic 6: best, fish, tri, serv, tikka 
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##  Topic 8: perfect, well, chees, realli, fri 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, servic, food, ambienc, great 
##  Topic 2: chicken, tast, order, biryani, rice 
##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, one, food 
##  Topic 5: food, visit, staff, friend, love 
##  Topic 6: fish, tri, best, serv, kebab 
##  Topic 7: even, bad, take, deliveri, chocol 
##  Topic 8: perfect, well, chees, sauc, fri 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, servic, food, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: food, visit, staff, friend, love 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
##  Topic 8: perfect, well, chees, sauc, fri 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, servic, food, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, dish, food 
##  Topic 5: visit, food, staff, friend, love 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: bad, even, take, deliveri, chocol 
##  Topic 8: perfect, well, chees, sauc, noodl 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, experi 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, food, love 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: bad, even, take, deliveri, chocol 
##  Topic 8: perfect, well, chees, sauc, noodl 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, food 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: bad, even, take, deliveri, chocol 
##  Topic 8: perfect, chees, well, sauc, noodl 
##  Topic 9: order, time, food, serv, just 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: bad, even, take, deliveri, chocol 
##  Topic 8: perfect, chees, well, sauc, noodl 
##  Topic 9: order, time, serv, food, just 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: fish, tri, best, kebab, serv 
##  Topic 7: bad, even, take, deliveri, chocol 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: fish, tri, kebab, best, tikka 
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##  Topic 9: order, time, serv, just, food 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: fish, tri, kebab, best, tikka 
##  Topic 7: bad, even, take, deliveri, chocol 
##  Topic 8: perfect, chees, well, sauc, noodl 
##  Topic 9: order, time, serv, just, food 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, dish, one 
##  Topic 5: visit, staff, friend, love, best 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
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##  Topic 6: fish, tri, kebab, best, serv 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
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##  Topic 6: fish, tri, kebab, best, serv 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
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##  Topic 6: fish, tri, kebab, best, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
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##  Topic 6: fish, tri, kebab, best, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
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##  Topic 6: fish, tri, kebab, best, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
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##  Topic 6: fish, tri, kebab, best, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
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##  Topic 6: fish, tri, kebab, best, serv 
##  Topic 7: even, bad, take, deliveri, chocol 
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##  Topic 9: order, time, serv, just, dont 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 6: fish, tri, kebab, serv, best 
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## Topic 1: good, food, servic, ambienc, great 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 6: chicken, fish, tri, kebab, tikka 
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## Topic 1: good, food, servic, ambienc, great 
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##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
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## Completing Iteration 416 (approx. per word bound = -6.209, relative change = 1.662e-05) 
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## Completing Iteration 420 (approx. per word bound = -6.209, relative change = 1.832e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, tast, order, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, even, deliveri, take, chocol 
##  Topic 8: perfect, chees, sauc, noodl, fri 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
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## Completing Iteration 421 (approx. per word bound = -6.208, relative change = 1.873e-05) 
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## Completing Iteration 425 (approx. per word bound = -6.208, relative change = 1.984e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, tast, order, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, even, deliveri, take, chocol 
##  Topic 8: perfect, chees, sauc, noodl, tast 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 426 (approx. per word bound = -6.208, relative change = 1.984e-05) 
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## Completing Iteration 428 (approx. per word bound = -6.208, relative change = 1.937e-05) 
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## Completing Iteration 430 (approx. per word bound = -6.207, relative change = 2.032e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, even, deliveri, take, chocol 
##  Topic 8: perfect, chees, tast, sauc, noodl 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 431 (approx. per word bound = -6.207, relative change = 2.120e-05) 
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## Completing Iteration 432 (approx. per word bound = -6.207, relative change = 2.184e-05) 
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## Completing Iteration 433 (approx. per word bound = -6.207, relative change = 2.201e-05) 
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## Completing Iteration 435 (approx. per word bound = -6.207, relative change = 2.134e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, deliveri, take, even, chocol 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
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## Completing Iteration 436 (approx. per word bound = -6.207, relative change = 2.088e-05) 
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## Completing Iteration 437 (approx. per word bound = -6.206, relative change = 2.053e-05) 
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## Completing Iteration 438 (approx. per word bound = -6.206, relative change = 2.075e-05) 
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## Completing Iteration 440 (approx. per word bound = -6.206, relative change = 2.218e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, deliveri, take, chocol, cake 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
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## Completed E-Step (0 seconds). 
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## Completing Iteration 441 (approx. per word bound = -6.206, relative change = 2.297e-05) 
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## Completing Iteration 445 (approx. per word bound = -6.205, relative change = 2.333e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, kebab, tikka 
##  Topic 7: bad, deliveri, take, chocol, cake 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, just, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 446 (approx. per word bound = -6.205, relative change = 2.260e-05) 
## ........................................................................................................
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## Completing Iteration 447 (approx. per word bound = -6.205, relative change = 2.215e-05) 
## ........................................................................................................
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## Completing Iteration 448 (approx. per word bound = -6.205, relative change = 2.202e-05) 
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## Completing Iteration 449 (approx. per word bound = -6.205, relative change = 2.162e-05) 
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## Completing Iteration 450 (approx. per word bound = -6.205, relative change = 2.102e-05) 
## Topic 1: good, food, servic, ambienc, great 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, take, chocol, cake 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, food, just 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 451 (approx. per word bound = -6.205, relative change = 2.023e-05) 
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## Completing Iteration 452 (approx. per word bound = -6.204, relative change = 2.010e-05) 
## ........................................................................................................
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## Completing Iteration 453 (approx. per word bound = -6.204, relative change = 2.087e-05) 
## ........................................................................................................
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## Completing Iteration 454 (approx. per word bound = -6.204, relative change = 2.207e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 455 (approx. per word bound = -6.204, relative change = 2.209e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, take, cake 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, food, just 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
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## Completing Iteration 456 (approx. per word bound = -6.204, relative change = 2.081e-05) 
## ........................................................................................................
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## Completing Iteration 457 (approx. per word bound = -6.204, relative change = 1.891e-05) 
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## Completing Iteration 458 (approx. per word bound = -6.204, relative change = 1.751e-05) 
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## Completing Iteration 459 (approx. per word bound = -6.204, relative change = 1.703e-05) 
## ........................................................................................................
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## Completed M-Step. 
## Completing Iteration 460 (approx. per word bound = -6.203, relative change = 1.698e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, take 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, food, even 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 461 (approx. per word bound = -6.203, relative change = 1.667e-05) 
## ........................................................................................................
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## Completing Iteration 462 (approx. per word bound = -6.203, relative change = 1.647e-05) 
## ........................................................................................................
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## Completing Iteration 463 (approx. per word bound = -6.203, relative change = 1.637e-05) 
## ........................................................................................................
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## Completing Iteration 464 (approx. per word bound = -6.203, relative change = 1.591e-05) 
## ........................................................................................................
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## Completed M-Step. 
## Completing Iteration 465 (approx. per word bound = -6.203, relative change = 1.583e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, take 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, food, even 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 466 (approx. per word bound = -6.203, relative change = 1.601e-05) 
## ........................................................................................................
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## Completing Iteration 467 (approx. per word bound = -6.203, relative change = 1.660e-05) 
## ........................................................................................................
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## Completing Iteration 468 (approx. per word bound = -6.203, relative change = 1.759e-05) 
## ........................................................................................................
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## Completing Iteration 469 (approx. per word bound = -6.203, relative change = 1.868e-05) 
## ........................................................................................................
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## Completed M-Step. 
## Completing Iteration 470 (approx. per word bound = -6.202, relative change = 1.956e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, deliv 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, even, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 471 (approx. per word bound = -6.202, relative change = 2.021e-05) 
## ........................................................................................................
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## Completing Iteration 472 (approx. per word bound = -6.202, relative change = 1.949e-05) 
## ........................................................................................................
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## Completing Iteration 473 (approx. per word bound = -6.202, relative change = 1.820e-05) 
## ........................................................................................................
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## Completing Iteration 474 (approx. per word bound = -6.202, relative change = 1.778e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 475 (approx. per word bound = -6.202, relative change = 1.765e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: chicken, order, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, deliv 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, even, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 476 (approx. per word bound = -6.202, relative change = 1.734e-05) 
## ........................................................................................................
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## Completing Iteration 477 (approx. per word bound = -6.202, relative change = 1.617e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 478 (approx. per word bound = -6.202, relative change = 1.496e-05) 
## ........................................................................................................
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## Completing Iteration 479 (approx. per word bound = -6.201, relative change = 1.406e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 480 (approx. per word bound = -6.201, relative change = 1.383e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: order, chicken, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, deliv 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, even, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 481 (approx. per word bound = -6.201, relative change = 1.443e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 482 (approx. per word bound = -6.201, relative change = 1.486e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 483 (approx. per word bound = -6.201, relative change = 1.386e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 484 (approx. per word bound = -6.201, relative change = 1.257e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 485 (approx. per word bound = -6.201, relative change = 1.235e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: order, chicken, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, deliv 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, even, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 486 (approx. per word bound = -6.201, relative change = 1.380e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 487 (approx. per word bound = -6.201, relative change = 1.670e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 488 (approx. per word bound = -6.201, relative change = 1.732e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 489 (approx. per word bound = -6.201, relative change = 1.262e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 490 (approx. per word bound = -6.200, relative change = 1.007e-05) 
## Topic 1: good, food, servic, great, ambienc 
##  Topic 2: order, chicken, tast, biryani, rice 
##  Topic 3: place, nice, realli, awesom, recommend 
##  Topic 4: like, tri, restaur, one, dish 
##  Topic 5: visit, staff, friend, love, best 
##  Topic 6: chicken, fish, tri, tikka, kebab 
##  Topic 7: bad, deliveri, chocol, cake, deliv 
##  Topic 8: perfect, tast, chees, sauc, noodl 
##  Topic 9: order, time, serv, even, food 
##  Topic 10: starter, veg, buffet, overal, menu 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Model Converged

model_15 <- stm(docs, vocab, K = 15, data = meta, init.type = "Spectral")

## Beginning Spectral Initialization 
##   Calculating the gram matrix...
##   Finding anchor words...
##      ...............
##   Recovering initialization...
##      ..................
## Initialization complete.
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 1 (approx. per word bound = -6.557) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 2 (approx. per word bound = -6.448, relative change = 1.670e-02) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 3 (approx. per word bound = -6.360, relative change = 1.357e-02) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 4 (approx. per word bound = -6.303, relative change = 8.942e-03) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 5 (approx. per word bound = -6.266, relative change = 5.932e-03) 
## Topic 1: food, just, order, bad, place 
##  Topic 2: good, servic, deliveri, dinner, will 
##  Topic 3: love, awesom, must, best, ever 
##  Topic 4: place, food, realli, great, tast 
##  Topic 5: serv, tast, one, place, restaur 
##  Topic 6: experi, order, friend, tast, food 
##  Topic 7: amaz, visit, best, place, chicken 
##  Topic 8: buffet, one, starter, like, cake 
##  Topic 9: chicken, friend, food, place, recommend 
##  Topic 10: place, tri, can, visit, chicken 
##  Topic 11: food, place, nice, ambienc, great 
##  Topic 12: chicken, order, food, paneer, biryani 
##  Topic 13: good, tast, order, less, starter 
##  Topic 14: order, time, tast, chicken, alway 
##  Topic 15: food, like, time, order, good 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 6 (approx. per word bound = -6.241, relative change = 4.046e-03) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 7 (approx. per word bound = -6.222, relative change = 2.921e-03) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 8 (approx. per word bound = -6.208, relative change = 2.254e-03) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 9 (approx. per word bound = -6.198, relative change = 1.742e-03) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Completing Iteration 10 (approx. per word bound = -6.189, relative change = 1.333e-03) 
## Topic 1: food, order, bad, just, even 
##  Topic 2: good, servic, deliveri, will, dinner 
##  Topic 3: love, awesom, best, must, come 
##  Topic 4: place, realli, food, even, great 
##  Topic 5: serv, ask, one, restaur, manag 
##  Topic 6: friend, experi, order, provid, tast 
##  Topic 7: amaz, visit, best, place, guy 
##  Topic 8: buffet, starter, main, cake, chocol 
##  Topic 9: friend, chicken, place, food, excel 
##  Topic 10: place, tri, can, visit, biryani 
##  Topic 11: food, place, nice, ambienc, great 
##  Topic 12: chicken, order, biryani, paneer, food 
##  Topic 13: tast, veg, quantiti, chicken, order 
##  Topic 14: time, order, tast, alway, sauc 
##  Topic 15: food, like, chicken, realli, just 
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## Topic 1: food, order, bad, just, even 
##  Topic 2: good, servic, deliveri, will, dinner 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: realli, place, even, food, drink 
##  Topic 5: serv, ask, manag, one, tabl 
##  Topic 6: friend, provid, experi, super, staff 
##  Topic 7: visit, amaz, place, best, fast 
##  Topic 8: starter, buffet, main, cours, chocol 
##  Topic 9: friend, chicken, excel, tri, well 
##  Topic 10: place, tri, can, visit, bit 
##  Topic 11: food, place, nice, ambienc, great 
##  Topic 12: biryani, chicken, order, paneer, food 
##  Topic 13: tast, chicken, veg, order, quantiti 
##  Topic 14: time, order, tast, alway, sauc 
##  Topic 15: chicken, food, like, realli, crispi 
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## Completing Iteration 16 (approx. per word bound = -6.164, relative change = 4.317e-04) 
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## Topic 1: food, order, bad, just, restaur 
##  Topic 2: good, servic, deliveri, will, dinner 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: realli, place, even, drink, music 
##  Topic 5: serv, ask, manag, tabl, one 
##  Topic 6: friend, provid, super, experi, indian 
##  Topic 7: amaz, visit, place, delici, well 
##  Topic 8: starter, buffet, main, cours, chocol 
##  Topic 9: chicken, excel, friend, burger, tri 
##  Topic 10: place, tri, can, bit, visit 
##  Topic 11: place, food, nice, great, ambienc 
##  Topic 12: biryani, chicken, order, paneer, mutton 
##  Topic 13: tast, chicken, order, veg, quantiti 
##  Topic 14: time, order, alway, tast, sauc 
##  Topic 15: chicken, food, like, shake, soup 
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## Completing Iteration 21 (approx. per word bound = -6.153, relative change = 3.180e-04) 
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## Topic 1: food, order, bad, restaur, just 
##  Topic 2: good, servic, deliveri, will, visit 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: realli, place, drink, even, music 
##  Topic 5: serv, ask, manag, tabl, one 
##  Topic 6: provid, super, friend, indian, experi 
##  Topic 7: amaz, visit, delici, well, place 
##  Topic 8: starter, buffet, main, cours, lunch 
##  Topic 9: chicken, excel, burger, tri, chees 
##  Topic 10: tri, place, can, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, order, paneer, mutton 
##  Topic 13: tast, chicken, order, veg, quantiti 
##  Topic 14: time, order, alway, sauc, deliv 
##  Topic 15: chicken, soup, food, corn, like 
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## Completing Iteration 26 (approx. per word bound = -6.145, relative change = 2.319e-04) 
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## Topic 1: food, order, restaur, bad, just 
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##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: drink, place, realli, even, music 
##  Topic 5: serv, ask, manag, tabl, custom 
##  Topic 6: provid, super, indian, hospit, restaur 
##  Topic 7: amaz, visit, delici, well, realli 
##  Topic 8: starter, buffet, main, cours, lunch 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, place, can, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, order, paneer, curri 
##  Topic 13: tast, order, chicken, veg, quantiti 
##  Topic 14: time, order, sauc, deliv, alway 
##  Topic 15: soup, corn, chicken, food, like 
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## Completing Iteration 35 (approx. per word bound = -6.136, relative change = 1.225e-04) 
## Topic 1: food, order, restaur, bad, just 
##  Topic 2: good, servic, deliveri, will, visit 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: drink, place, music, even, night 
##  Topic 5: serv, ask, manag, tabl, custom 
##  Topic 6: provid, super, indian, hospit, restaur 
##  Topic 7: amaz, visit, delici, well, help 
##  Topic 8: starter, buffet, main, cours, lunch 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, quantiti, qualiti 
##  Topic 14: time, order, deliv, noodl, sauc 
##  Topic 15: soup, corn, chicken, food, shake 
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## Completing Iteration 36 (approx. per word bound = -6.135, relative change = 1.132e-04) 
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## Completing Iteration 37 (approx. per word bound = -6.135, relative change = 1.091e-04) 
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## Completing Iteration 40 (approx. per word bound = -6.133, relative change = 9.613e-05) 
## Topic 1: food, order, restaur, bad, just 
##  Topic 2: good, servic, will, deliveri, visit 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: drink, music, place, even, night 
##  Topic 5: serv, ask, manag, tabl, custom 
##  Topic 6: provid, super, indian, restaur, hospit 
##  Topic 7: amaz, delici, visit, well, thank 
##  Topic 8: starter, buffet, main, cours, lunch 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, quantiti, qualiti 
##  Topic 14: time, order, noodl, deliv, sauc 
##  Topic 15: soup, corn, chicken, shake, food 
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## Completing Iteration 41 (approx. per word bound = -6.132, relative change = 9.097e-05) 
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## Completing Iteration 43 (approx. per word bound = -6.131, relative change = 8.174e-05) 
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## Completing Iteration 45 (approx. per word bound = -6.130, relative change = 6.298e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, will, deliveri, visit 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: drink, music, place, even, night 
##  Topic 5: serv, ask, manag, tabl, custom 
##  Topic 6: provid, super, indian, restaur, hospit 
##  Topic 7: amaz, delici, well, thank, visit 
##  Topic 8: starter, buffet, main, cours, veg 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, quantiti, also 
##  Topic 14: time, order, noodl, deliv, sauc 
##  Topic 15: soup, corn, chicken, shake, food 
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## Completing Iteration 46 (approx. per word bound = -6.130, relative change = 6.015e-05) 
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## Completing Iteration 48 (approx. per word bound = -6.129, relative change = 6.714e-05) 
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## Completing Iteration 50 (approx. per word bound = -6.128, relative change = 7.557e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, will, deliveri, visit 
##  Topic 3: love, best, awesom, must, everyth 
##  Topic 4: drink, music, place, night, even 
##  Topic 5: serv, ask, manag, custom, tabl 
##  Topic 6: provid, super, indian, restaur, hospit 
##  Topic 7: amaz, delici, thank, well, visit 
##  Topic 8: starter, buffet, veg, main, cours 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, quantiti 
##  Topic 14: time, order, noodl, deliv, sauc 
##  Topic 15: soup, corn, chicken, shake, food 
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## Completing Iteration 51 (approx. per word bound = -6.128, relative change = 7.227e-05) 
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## Completing Iteration 55 (approx. per word bound = -6.126, relative change = 5.982e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, will, deliveri, quick 
##  Topic 3: love, best, awesom, must, one 
##  Topic 4: drink, music, place, night, even 
##  Topic 5: serv, ask, manag, custom, tabl 
##  Topic 6: provid, super, indian, restaur, hospit 
##  Topic 7: amaz, delici, thank, well, visit 
##  Topic 8: starter, buffet, veg, main, cours 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, order, noodl, deliv, sauc 
##  Topic 15: soup, corn, chicken, shake, food 
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## Completing Iteration 56 (approx. per word bound = -6.126, relative change = 5.825e-05) 
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## Completing Iteration 59 (approx. per word bound = -6.125, relative change = 5.233e-05) 
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## Completing Iteration 60 (approx. per word bound = -6.125, relative change = 5.203e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, will, quick 
##  Topic 3: love, best, awesom, must, one 
##  Topic 4: drink, music, place, night, even 
##  Topic 5: serv, ask, manag, custom, tabl 
##  Topic 6: provid, super, indian, restaur, hospit 
##  Topic 7: amaz, delici, thank, well, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, order, noodl, deliv, sauc 
##  Topic 15: soup, corn, chicken, shake, veg 
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## Completing Iteration 61 (approx. per word bound = -6.124, relative change = 5.233e-05) 
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## Completing Iteration 63 (approx. per word bound = -6.124, relative change = 5.465e-05) 
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## Completing Iteration 65 (approx. per word bound = -6.123, relative change = 5.842e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, will, quick 
##  Topic 3: love, best, awesom, must, one 
##  Topic 4: drink, music, place, night, even 
##  Topic 5: serv, ask, manag, custom, tabl 
##  Topic 6: provid, super, indian, restaur, vegetarian 
##  Topic 7: amaz, delici, thank, well, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, order, noodl, sauc, deliv 
##  Topic 15: soup, corn, chicken, shake, like 
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## Completing Iteration 66 (approx. per word bound = -6.123, relative change = 5.638e-05) 
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## Completing Iteration 67 (approx. per word bound = -6.122, relative change = 5.334e-05) 
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## Completing Iteration 68 (approx. per word bound = -6.122, relative change = 4.812e-05) 
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## Completing Iteration 69 (approx. per word bound = -6.122, relative change = 4.499e-05) 
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## Completing Iteration 70 (approx. per word bound = -6.122, relative change = 4.353e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, will, ambienc 
##  Topic 3: love, best, awesom, must, one 
##  Topic 4: drink, music, place, night, crowd 
##  Topic 5: serv, ask, custom, manag, tabl 
##  Topic 6: provid, super, indian, vegetarian, restaur 
##  Topic 7: amaz, delici, well, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, excel, tri, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, order, noodl, sauc, deliv 
##  Topic 15: soup, corn, chicken, shake, like 
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## Completing Iteration 71 (approx. per word bound = -6.121, relative change = 4.249e-05) 
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## Completing Iteration 73 (approx. per word bound = -6.121, relative change = 3.947e-05) 
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## Completing Iteration 75 (approx. per word bound = -6.120, relative change = 4.286e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, will, ambienc 
##  Topic 3: love, best, awesom, must, one 
##  Topic 4: drink, music, place, night, crowd 
##  Topic 5: serv, custom, ask, manag, tabl 
##  Topic 6: provid, super, vegetarian, indian, restaur 
##  Topic 7: amaz, delici, well, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, tri, excel, burger, chees 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, order, curri 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, noodl, order, sauc, tast 
##  Topic 15: soup, corn, chicken, shake, like 
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## Completing Iteration 76 (approx. per word bound = -6.120, relative change = 5.114e-05) 
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## Completing Iteration 78 (approx. per word bound = -6.119, relative change = 5.296e-05) 
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## Completing Iteration 79 (approx. per word bound = -6.119, relative change = 5.796e-05) 
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## Completing Iteration 80 (approx. per word bound = -6.119, relative change = 6.591e-05) 
## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, ambienc, will 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: drink, music, place, night, crowd 
##  Topic 5: serv, custom, ask, manag, tabl 
##  Topic 6: provid, super, vegetarian, indian, restaur 
##  Topic 7: amaz, delici, well, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, tri, burger, chees, fish 
##  Topic 10: tri, can, place, bit, get 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: time, noodl, order, sauc, tast 
##  Topic 15: soup, corn, chicken, shake, like 
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## Completing Iteration 81 (approx. per word bound = -6.118, relative change = 7.418e-05) 
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## Topic 1: food, order, restaur, bad, time 
##  Topic 2: good, servic, deliveri, ambienc, quit 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: drink, place, music, night, crowd 
##  Topic 5: serv, custom, ask, manag, tabl 
##  Topic 6: provid, super, vegetarian, indian, restaur 
##  Topic 7: amaz, delici, well, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, tri, burger, chees, fish 
##  Topic 10: tri, can, place, bit, coffe 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, order, sauc, tast 
##  Topic 15: soup, corn, chicken, shake, like 
## ........................................................................................................
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## Completing Iteration 86 (approx. per word bound = -6.116, relative change = 5.372e-05) 
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## Completing Iteration 90 (approx. per word bound = -6.115, relative change = 4.504e-05) 
## Topic 1: order, food, restaur, bad, time 
##  Topic 2: good, servic, deliveri, ambienc, quit 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: serv, custom, ask, manag, tabl 
##  Topic 6: provid, super, vegetarian, indian, restaur 
##  Topic 7: amaz, delici, well, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, tri, chees, fish 
##  Topic 10: tri, can, place, cake, bit 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, order, sauc, tast 
##  Topic 15: soup, corn, chicken, shake, like 
## ........................................................................................................
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## Completing Iteration 91 (approx. per word bound = -6.115, relative change = 4.668e-05) 
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## Topic 1: order, food, restaur, bad, time 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: serv, custom, ask, manag, servic 
##  Topic 6: provid, super, vegetarian, indian, restaur 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, tri, chees, fish 
##  Topic 10: tri, can, place, cake, bit 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, sauc, tast, order 
##  Topic 15: soup, corn, chicken, shake, like 
## ........................................................................................................
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## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: serv, custom, ask, manag, servic 
##  Topic 6: provid, super, vegetarian, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, tri, chees, fish 
##  Topic 10: tri, can, cake, place, chocol 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, sauc, tast, chicken 
##  Topic 15: soup, corn, chicken, shake, sweet 
## ........................................................................................................
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## Completing Iteration 104 (approx. per word bound = -6.112, relative change = 2.287e-05) 
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## Completing Iteration 105 (approx. per word bound = -6.112, relative change = 2.126e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, awesom, one, must 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: serv, custom, ask, manag, servic 
##  Topic 6: provid, vegetarian, super, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, tri, fish 
##  Topic 10: tri, cake, can, chocol, place 
##  Topic 11: place, food, great, nice, ambienc 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, tast, sauc, chicken 
##  Topic 15: soup, corn, chicken, sweet, shake 
## ........................................................................................................
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## Completing Iteration 106 (approx. per word bound = -6.112, relative change = 1.997e-05) 
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## ........................................................................................................
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## Completing Iteration 108 (approx. per word bound = -6.111, relative change = 1.898e-05) 
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## Completing Iteration 110 (approx. per word bound = -6.111, relative change = 1.893e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, awesom, one, tri 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, serv, ask, servic, manag 
##  Topic 6: provid, vegetarian, super, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, tri, fish 
##  Topic 10: tri, cake, chocol, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, tast, sauc, chicken 
##  Topic 15: soup, corn, chicken, sweet, shake 
## ........................................................................................................
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## Completing Iteration 111 (approx. per word bound = -6.111, relative change = 1.995e-05) 
## ........................................................................................................
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## Completing Iteration 112 (approx. per word bound = -6.111, relative change = 2.088e-05) 
## ........................................................................................................
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## Completing Iteration 113 (approx. per word bound = -6.111, relative change = 1.991e-05) 
## ........................................................................................................
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## Completing Iteration 115 (approx. per word bound = -6.111, relative change = 1.761e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, awesom, tri, one 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, serv, servic, ask, manag 
##  Topic 6: provid, vegetarian, super, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, tri, fish 
##  Topic 10: chocol, cake, tri, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, order 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, tast, sauc, chicken 
##  Topic 15: soup, corn, chicken, sweet, like 
## ........................................................................................................
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## Completing Iteration 116 (approx. per word bound = -6.110, relative change = 1.697e-05) 
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## ........................................................................................................
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## Completing Iteration 120 (approx. per word bound = -6.110, relative change = 1.779e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, tri, awesom, one 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, serv, servic, ask, manag 
##  Topic 6: provid, super, vegetarian, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, fish, tri 
##  Topic 10: chocol, cake, tri, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, mutton 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, tast, sauc, chicken 
##  Topic 15: soup, corn, chicken, sweet, like 
## ........................................................................................................
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## Completing Iteration 121 (approx. per word bound = -6.110, relative change = 1.639e-05) 
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## Completing Iteration 122 (approx. per word bound = -6.110, relative change = 1.595e-05) 
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## Completing Iteration 123 (approx. per word bound = -6.110, relative change = 1.716e-05) 
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## Completing Iteration 125 (approx. per word bound = -6.109, relative change = 2.207e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, tri, awesom, one 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, serv, servic, ask, manag 
##  Topic 6: provid, super, vegetarian, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, fish, tri 
##  Topic 10: chocol, cake, tri, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, mutton 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, tast, sauc, chicken 
##  Topic 15: soup, corn, chicken, sweet, like 
## ........................................................................................................
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## Completing Iteration 126 (approx. per word bound = -6.109, relative change = 2.135e-05) 
## ........................................................................................................
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## Completing Iteration 127 (approx. per word bound = -6.109, relative change = 2.060e-05) 
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## Completing Iteration 128 (approx. per word bound = -6.109, relative change = 1.922e-05) 
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## Completing Iteration 130 (approx. per word bound = -6.109, relative change = 1.486e-05) 
## Topic 1: order, food, restaur, time, bad 
##  Topic 2: good, servic, ambienc, deliveri, quit 
##  Topic 3: love, best, tri, awesom, one 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, servic, serv, ask, manag 
##  Topic 6: provid, super, vegetarian, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: starter, veg, buffet, main, cours 
##  Topic 9: chicken, burger, chees, fish, tri 
##  Topic 10: chocol, cake, tri, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, mutton 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, time, chicken, tast, dish 
##  Topic 15: soup, corn, chicken, sweet, like 
## ........................................................................................................
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## Completing Iteration 131 (approx. per word bound = -6.109, relative change = 1.359e-05) 
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## Completing Iteration 132 (approx. per word bound = -6.109, relative change = 1.295e-05) 
## ........................................................................................................
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## Completing Iteration 133 (approx. per word bound = -6.109, relative change = 1.290e-05) 
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## Completing Iteration 134 (approx. per word bound = -6.109, relative change = 1.237e-05) 
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## Completing Iteration 135 (approx. per word bound = -6.108, relative change = 1.084e-05) 
## Topic 1: order, food, time, restaur, bad 
##  Topic 2: good, servic, ambienc, quit, tasti 
##  Topic 3: love, best, tri, awesom, one 
##  Topic 4: place, drink, music, night, crowd 
##  Topic 5: custom, servic, serv, ask, manag 
##  Topic 6: provid, super, vegetarian, indian, hospit 
##  Topic 7: amaz, well, delici, thank, serv 
##  Topic 8: veg, starter, buffet, main, cours 
##  Topic 9: chicken, burger, chees, fish, tri 
##  Topic 10: chocol, cake, tri, can, place 
##  Topic 11: place, food, great, nice, realli 
##  Topic 12: biryani, chicken, paneer, curri, mutton 
##  Topic 13: tast, order, chicken, also, qualiti 
##  Topic 14: noodl, chicken, tast, time, dish 
##  Topic 15: soup, corn, chicken, sweet, like 
## ........................................................................................................
## Completed E-Step (0 seconds). 
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## Completing Iteration 136 (approx. per word bound = -6.108, relative change = 1.007e-05) 
## ........................................................................................................
## Completed E-Step (0 seconds). 
## Completed M-Step. 
## Model Converged

model_20 <- stm(docs, vocab, K = 20, data = meta, init.type = "Spectral", verbose = FALSE)
model_25 <- stm(docs, vocab, K = 25, data = meta, init.type = "Spectral", verbose = FALSE)


labelTopics(model_10, n = 15)

## Topic 1 Top Words:
##       Highest Prob: good, food, servic, great, ambienc, also, time, well, qualiti, tasti, especi, alway, suggest, dinner, feel 
##       FREX: good, servic, great, ambienc, food, especi, breakfast, beyond, prompt, forward, doubt, profession, sizzler, provid, suggest 
##       Lift: awsom, desir, fellow, hasubul, jayanta, natur, raza, shuvro, stater, steward, tapan, testi, good, doubt, folk 
##       Score: good, servic, great, ambienc, food, time, especi, also, qualiti, alway, provid, fellow, sizzler, atmospher, quick 
## Topic 2 Top Words:
##       Highest Prob: order, chicken, tast, biryani, rice, quantiti, paneer, item, cook, less, piec, qualiti, curri, also, spici 
##       FREX: biryani, quantiti, butter, egg, biriyani, shawarma, gravi, dal, pulao, makhani, dum, pulav, kodi, haleem, cook 
##       Lift: bawarchi, birayani, burnt, dum, execut, gari, gravi, individu, jeera, junnu, kadai, kodi, lip, makhani, mamsam 
##       Score: chicken, biryani, order, quantiti, rice, piec, mutton, egg, paneer, tast, cook, shawarma, less, masala, spici 
## Topic 3 Top Words:
##       Highest Prob: place, nice, realli, awesom, recommend, experi, can, night, music, crowd, serv, hyderabad, parti, seat, one 
##       FREX: nice, awesom, parti, place, crowd, hangout, hang, night, danc, music, beer, outdoor, brew, fantast, realli 
##       Lift: -hous, aesthet, gang, hang, met, often, perform, accommod, breez, brew, depend, fantast, forest, liit, nice 
##       Score: place, nice, awesom, music, danc, hangout, parti, realli, crowd, floor, night, weekend, hang, outdoor, behaviour 
## Topic 4 Top Words:
##       Highest Prob: like, tri, restaur, one, dish, littl, much, price, better, didnt, rate, quit, expect, need, worth 
##       FREX: expect, satisfi, mani, rate, ala, littl, like, gold, didnt, kulcha, need, better, much, restaur, briyani 
##       Lift: dosa, nonveg, satisfi, ala, andhra, blow, chili, consider, court, crowdi, dhaba, digest, everybodi, expect, googl 
##       Score: restaur, like, expect, dish, zomato, tri, rate, briyani, need, mani, littl, mayonnais, much, didnt, satisfi 
## Topic 5 Top Words:
##       Highest Prob: visit, staff, friend, love, best, amaz, will, must, enjoy, excel, thank, ambianc, polit, definit, famili 
##       FREX: staff, excel, thank, visit, hospit, birthday, spend, polit, friend, courteous, enjoy, job, help, love, amaz 
##       Lift: assist, bdub, crew, english, explain, game, gestur, hospit, job, lightn, nitish, pradeep, product, raj, recept 
##       Score: visit, staff, friend, excel, thank, love, amaz, enjoy, best, polit, courteous, hospit, help, famili, ambianc 
## Topic 6 Top Words:
##       Highest Prob: chicken, fish, tri, tikka, kebab, place, serv, mandi, grill, prawn, best, starter, soup, one, wing 
##       FREX: kebab, mandi, grill, wing, prawn, bbq, mojito, fish, kheer, arabian, yummi, tikka, lemon, juici, marin 
##       Lift: arab, basa, daawat, gate, nugget, pahadi, papdi, photograph, queue, raan, sholay, tandoor, traffic, apollo, babycorn 
##       Score: chicken, mandi, kebab, fish, tikka, arabian, marin, wing, grill, juici, bbq, mutton, mojito, coriand, main 
## Topic 7 Top Words:
##       Highest Prob: bad, deliveri, chocol, cake, deliv, paratha, eat, cream, browni, never, coffe, ice, bill, got, one 
##       FREX: deliveri, cake, paratha, browni, coffe, bill, fast, wast, wrong, inform, milk, bad, deliv, bakeri, waffl 
##       Lift: advanc, blueberri, earn, alphonso, bcz, berri, bought, bouncer, boy, break, browni, cake, cancel, courtesi, cup 
##       Score: fast, deliveri, bad, cake, chocol, deliv, paratha, bill, browni, wast, coffe, cooki, cream, wrong, shake 
## Topic 8 Top Words:
##       Highest Prob: perfect, tast, chees, sauc, noodl, chilli, fri, realli, well, corn, sweet, pizza, just, dish, hot 
##       FREX: chees, noodl, chilli, momo, north, pork, punjabi, combo, veggi, aloo, pan, tomato, steam, sauc, heaven 
##       Lift: alfredo, beef, croissant, crust, innov, kimchi, lettuc, mexican, minc, stir, temptey, tofu, aloo, amritsari, atoneplaceonlinecom 
##       Score: chees, noodl, momo, chilli, pork, sauc, steam, corn, fri, punjabi, north, veggi, parantha, combo, rib 
## Topic 9 Top Words:
##       Highest Prob: order, time, serv, even, food, will, experi, just, ask, custom, worst, dont, manag, restaur, get 
##       FREX: ask, custom, worst, think, hour, pathet, horribl, maggi, manag, second, reach, told, wait, arriv, chai 
##       Lift: arriv, arrog, ask, awar, baker, bike, bun, burst, certain, chai, cheat, cleanli, cockroach, condition, custom 
##       Score: worst, order, custom, pathet, time, ask, maggi, ever, said, think, min, restaur, dont, rude, horribl 
## Topic 10 Top Words:
##       Highest Prob: starter, veg, buffet, overal, menu, went, main, option, cours, varieti, decent, averag, price, burger, drink 
##       FREX: buffet, bar, colleagu, option, donut, limit, vegetarian, varieti, weekday, screen, menu, burger, averag, non, overal 
##       Lift: addit, affair, avg, bore, breweri, caution, construct, coupon, cramp, dull, econom, exchang, fire, hindi, jam 
##       Score: buffet, starter, main, veg, cours, non, spread, bar, salti, option, burger, live, drink, dessert, play

plot.STM(model_10, main = "Top Topics (K = 10)", n = 7)

get_metrics <- function(model, K, label) {
  data.frame(
    K = 1:K,
    Exclusivity = exclusivity(model),
    SemanticCoherence = semanticCoherence(model = model, docs),
    Model = label
  )
}

M10 <- get_metrics(model_10, 10, "K=10")
M15 <- get_metrics(model_15, 15, "K=15")
M20 <- get_metrics(model_20, 20, "K=20")
M25 <- get_metrics(model_25, 25, "K=25")


mean_metrics <- data.frame(
  Model = c("K=10", "K=15", "K=20", "K=25"),
  Mean_Exclusivity = c(
    mean(exclusivity(model_10)),
    mean(exclusivity(model_15)),
    mean(exclusivity(model_20)),
    mean(exclusivity(model_25))
  ),
  Mean_Coherence = c(
    mean(semanticCoherence(model_10, docs)),
    mean(semanticCoherence(model_15, docs)),
    mean(semanticCoherence(model_20, docs)),
    mean(semanticCoherence(model_25, docs))
  )
)

mean_metrics

ModsExSem <- rbind(M10, M15, M20, M25)

ggplot(ModsExSem, aes(SemanticCoherence, Exclusivity, color = Model)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_text(aes(label = K), nudge_x = 0.03, nudge_y = 0.03, size = 3) +
  labs(
    title = "Semantic Coherence vs Exclusivity (STM Models)",
    x = "Semantic Coherence",
    y = "Exclusivity"
  ) +
  theme_minimal()

For the purpose of analysis model with 10 topics is chosen. Exclusivity and coherence values are reasonable and 10 seems like an appropriate number of topics, coherence is the best out of 4 models, which will prevent overlap of topics.

model_NP_10Prrateby<-stm(docs, vocab, K=10, data=meta, init.type = "Spectral", verbose=FALSE)
labelTopics(model_NP_10Prrateby)

## Topic 1 Top Words:
##       Highest Prob: good, food, servic, great, ambienc, also, time 
##       FREX: good, servic, great, ambienc, food, especi, breakfast 
##       Lift: awsom, desir, fellow, hasubul, jayanta, natur, raza 
##       Score: good, servic, great, ambienc, food, time, especi 
## Topic 2 Top Words:
##       Highest Prob: order, chicken, tast, biryani, rice, quantiti, paneer 
##       FREX: biryani, quantiti, butter, egg, biriyani, shawarma, gravi 
##       Lift: bawarchi, birayani, burnt, dum, execut, gari, gravi 
##       Score: chicken, biryani, order, quantiti, rice, piec, mutton 
## Topic 3 Top Words:
##       Highest Prob: place, nice, realli, awesom, recommend, experi, can 
##       FREX: nice, awesom, parti, place, crowd, hangout, hang 
##       Lift: -hous, aesthet, gang, hang, met, often, perform 
##       Score: place, nice, awesom, music, danc, hangout, parti 
## Topic 4 Top Words:
##       Highest Prob: like, tri, restaur, one, dish, littl, much 
##       FREX: expect, satisfi, mani, rate, ala, littl, like 
##       Lift: dosa, nonveg, satisfi, ala, andhra, blow, chili 
##       Score: restaur, like, expect, dish, zomato, tri, rate 
## Topic 5 Top Words:
##       Highest Prob: visit, staff, friend, love, best, amaz, will 
##       FREX: staff, excel, thank, visit, hospit, birthday, spend 
##       Lift: assist, bdub, crew, english, explain, game, gestur 
##       Score: visit, staff, friend, excel, thank, love, amaz 
## Topic 6 Top Words:
##       Highest Prob: chicken, fish, tri, tikka, kebab, place, serv 
##       FREX: kebab, mandi, grill, wing, prawn, bbq, mojito 
##       Lift: arab, basa, daawat, gate, nugget, pahadi, papdi 
##       Score: chicken, mandi, kebab, fish, tikka, arabian, marin 
## Topic 7 Top Words:
##       Highest Prob: bad, deliveri, chocol, cake, deliv, paratha, eat 
##       FREX: deliveri, cake, paratha, browni, coffe, bill, fast 
##       Lift: advanc, blueberri, earn, alphonso, bcz, berri, bought 
##       Score: fast, deliveri, bad, cake, chocol, deliv, paratha 
## Topic 8 Top Words:
##       Highest Prob: perfect, tast, chees, sauc, noodl, chilli, fri 
##       FREX: chees, noodl, chilli, momo, north, pork, punjabi 
##       Lift: alfredo, beef, croissant, crust, innov, kimchi, lettuc 
##       Score: chees, noodl, momo, chilli, pork, sauc, steam 
## Topic 9 Top Words:
##       Highest Prob: order, time, serv, even, food, will, experi 
##       FREX: ask, custom, worst, think, hour, pathet, horribl 
##       Lift: arriv, arrog, ask, awar, baker, bike, bun 
##       Score: worst, order, custom, pathet, time, ask, maggi 
## Topic 10 Top Words:
##       Highest Prob: starter, veg, buffet, overal, menu, went, main 
##       FREX: buffet, bar, colleagu, option, donut, limit, vegetarian 
##       Lift: addit, affair, avg, bore, breweri, caution, construct 
##       Score: buffet, starter, main, veg, cours, non, spread

pdf("stm_frex_labels.pdf", width = 14, height = 10)
par(mfrow = c(1,2))
plot(model_NP_10Prrateby, type = 'labels', labeltype = 'frex', main = 'FREX',text.cex=1, n=8)
plot(model_NP_10Prrateby, type = 'labels', labeltype = 'prob', main = 'PROB',text.cex=1, n=8)
dev.off()

## png 
##   2

After the inspection of plot topics were labeled. Results are presented in the array below where topics are prepared for mapping.

topic_labels <- c(
  "Service",
  "Food",
  "Night Time",
  "Good Impression",
  "Staff",
  "BBQ Dishes",
  "Desserts",
  "Main Dishes",
  "Negative Experience",
  "Buffet Options"
)
theta <- model_NP_10Prrateby$theta
colnames(theta) <- topic_labels
topic_means <- colMeans(theta)
par(mar = c(12, 4, 2, 2))
barplot(topic_means,
        las = 2,
        main = "Topic Proportions",
        ylab = "Mean Proportion")

Plot demonstrates reasonable picture of topics proportion - Service, Food and Negative Experience are represented more than specific topics, for example BBQ Dishes or Main Dishes.

plot(model_NP_10Prrateby,
     type   = "perspectives",
     topics = c(6, 7),
     main   = "Topic 6 (BBQ Dishes) vs Topic 7 (Desserts)")

plot(model_NP_10Prrateby,
     type   = "perspectives",
     topics = c(7, 8),
     main   = "Topic 7 (Desserts) vs Topic 8 (Main Dishes)")

td_theta <- tidytext::tidy(model_NP_10Prrateby, matrix = "theta")
selectiontdthteta<-td_theta[td_theta$document%in%c(1:12),]
thetaplot1<-ggplot(selectiontdthteta, aes(y=gamma, x=as.factor(topic), fill = as.factor(topic))) + geom_bar(stat="identity",alpha = 0.8, show.legend = FALSE) + facet_wrap(~ document,
ncol = 3) + labs(title = "Theta values per document", y = expression(theta), x = "Topic")
thetaplot1

On the plot topic distribution in different documents could be seen. For instance, documents 4 and 7 contain most probably negative review, while documents 2 and 9 describe probably staff and service in good way. In some documents, like 10, 11 and 12 topics are distributed more or less evenly.

# word probabilities in topics
pdf("word_probabilities_in_topics.pdf", width = 14, height = 10)

td_beta <- tidytext::tidy(model_NP_10Prrateby)
top_terms <- do.call(rbind, lapply(unique(td_beta$topic), function(t) {
  sub <- td_beta[td_beta$topic == t, ]
  sub <- sub[order(-sub$beta), ][1:10, ]
  sub$topic_label <- topic_labels[t]
  
  sub
}))


top_terms$topic_label <- factor(top_terms$topic_label, levels = topic_labels)
ggplot(top_terms, aes(x = reorder(term, beta), y = beta, fill = topic_label)) +
  geom_col(alpha = 0.8, show.legend = FALSE) +
  facet_wrap(~ topic_label, scales = "free_y") +
  coord_flip() +
  labs(
    x = NULL,
    y = expression(beta),
    title = "Highest word probabilities for each topic",
    subtitle = "Different words are associated with different topics"
  ) +
  theme_minimal(base_size = 14)

dev.off()

## png 
##   2

#word probabilities in topic 9 (Negative experience)
pdf("word_prob_top_9.pdf", width = 14, height = 10)
betaT9 <- td_beta[td_beta$topic == 9, ] 
betaT9$topic_label <- topic_labels[9] 
betaT9 <- betaT9[betaT9$beta > 0.005, ]
# Your plot
betaplotT9 <- ggplot(betaT9, aes(x = reorder(term, beta), y = beta, fill = topic_label)) +
  geom_col(alpha = 0.8, show.legend = FALSE) +
  coord_flip() +
  labs(
    x = "Terms",
    y = expression(beta),
    title = "Word probabilities for Topic 9 (Negative Experience)"
  ) +
  theme_minimal(base_size = 14)
print(betaplotT9)

dev.off()

## png 
##   2

#graph of topic correlations 
pdf("topic_correlation.pdf", width = 14, height = 10)
mod.out.corr <- topicCorr(model_NP_10Prrateby)
plot(mod.out.corr,vlabels = topic_labels)
dev.off()

## png 
##   2

library(stmCorrViz)

stmCorrViz(model_NP_10Prrateby, "stm-interactive-correlation.html",
 documents_raw=data$documents, documents_matrix=prep$documents)

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#model with covariate rating 
model_NP_10Prrateby_Sent<-stm(docs, vocab, prevalence=~rating, K=10, data=meta, init.type = "Spectral", verbose=FALSE)
model_NP_10Prrateby_Sent

## A topic model with 10 topics, 1984 documents and a 1873 word dictionary.

labelTopics(model_NP_10Prrateby_Sent)

## Topic 1 Top Words:
##       Highest Prob: good, food, servic, ambienc, great, time, also 
##       FREX: good, great, ambienc, servic, food, atmospher, prompt 
##       Lift: fellow, atmospher, awsom, desir, good, hasubul, inconveni 
##       Score: good, great, servic, ambienc, food, time, also 
## Topic 2 Top Words:
##       Highest Prob: order, chicken, tast, biryani, rice, quantiti, paneer 
##       FREX: biryani, quantiti, cook, egg, shawarma, wrap, tandoori 
##       Lift: bawarchi, birayani, cauliflow, dum, execut, gari, gravi 
##       Score: chicken, biryani, order, quantiti, rice, piec, mutton 
## Topic 3 Top Words:
##       Highest Prob: place, can, music, drink, even, night, crowd 
##       FREX: music, floor, danc, parti, crowd, beer, outdoor 
##       Lift: bare, caution, danc, dull, floor, gang, girl 
##       Score: place, music, danc, floor, parti, night, crowd 
## Topic 4 Top Words:
##       Highest Prob: like, tri, restaur, dish, one, littl, much 
##       FREX: expect, littl, rate, like, didnt, need, much 
##       Lift: dosa, overcook, shikanji, ala, andhra, briyani, brunch 
##       Score: restaur, like, expect, rate, dish, zomato, need 
## Topic 5 Top Words:
##       Highest Prob: nice, visit, friend, staff, love, realli, best 
##       FREX: thank, excel, nice, polit, birthday, awesom, friend 
##       Lift: -hous, bdub, bike, game, gestur, job, komatos 
##       Score: nice, awesom, amaz, staff, love, friend, excel 
## Topic 6 Top Words:
##       Highest Prob: chicken, tri, place, best, fish, kebab, one 
##       FREX: mandi, bbq, mojito, kebab, yummi, arabian, wing 
##       Lift: alfredo, bbq, croissant, daawat, distinct, exot, kinda 
##       Score: chicken, kebab, mandi, arabian, marin, fish, juici 
## Topic 7 Top Words:
##       Highest Prob: deliveri, chocol, cake, time, order, deliv, paratha 
##       FREX: deliveri, cake, deliv, paratha, fast, hotel, chocol 
##       Lift: blueberri, insist, cake, deliveri, alphonso, bakeri, bcz 
##       Score: fast, deliveri, chocol, cake, paratha, deliv, browni 
## Topic 8 Top Words:
##       Highest Prob: pizza, chees, sauc, tast, realli, tri, chilli 
##       FREX: chees, chines, momo, north, pork, combo, veggi 
##       Lift: beef, bhatur, cheesi, chines, copi, core, crunchi 
##       Score: chees, momo, sauc, noodl, chines, pork, north 
## Topic 9 Top Words:
##       Highest Prob: food, order, bad, servic, even, time, worst 
##       FREX: bad, worst, bill, pathet, poor, smell, wast 
##       Lift: advanc, arrog, burst, cancel, cheat, cockroach, condition 
##       Score: worst, bad, pathet, wast, order, bill, rude 
## Topic 10 Top Words:
##       Highest Prob: veg, starter, buffet, main, overal, cours, lunch 
##       FREX: buffet, cours, non, veg, vegetarian, decent, lunch 
##       Lift: addit, affair, avg, bore, construct, cramp, domin 
##       Score: veg, starter, main, buffet, cours, non, spread

topic_labels_rating <- c(
  "Service",
  "Food",
  "Night Time",
  "Good Impression",
  "Staff",
  "BBQ Dishes",
  "Desserts & Delivery",
  "Main Dishes",
  "Negative Experience",
  "Buffet Options"
)

predict_topics_NP<-estimateEffect(formula =~ rating, stmobj = model_NP_10Prrateby_Sent,
 metadata = prep$meta, uncertainty = "Global")
head(summary(predict_topics_NP))

## $call
## estimateEffect(formula = ~rating, stmobj = model_NP_10Prrateby_Sent, 
##     metadata = prep$meta, uncertainty = "Global")
## 
## $topics
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## $tables
## $tables[[1]]
##                Estimate  Std. Error   t value      Pr(>|t|)
## (Intercept) -0.01109529 0.006238432 -1.778539  7.546865e-02
## rating       0.04195302 0.001741451 24.090838 1.098926e-112
## 
## $tables[[2]]
##                Estimate  Std. Error   t value     Pr(>|t|)
## (Intercept)  0.22174402 0.011243732  19.72157 3.373946e-79
## rating      -0.02826566 0.002703743 -10.45427 6.154464e-25
## 
## $tables[[3]]
##               Estimate  Std. Error  t value     Pr(>|t|)
## (Intercept) 0.04616746 0.006688399 6.902617 6.847565e-12
## rating      0.01393052 0.001804427 7.720190 1.828973e-14
## 
## $tables[[4]]
##                 Estimate  Std. Error   t value    Pr(>|t|)
## (Intercept)  0.112890576 0.005119703 22.050218 1.52723e-96
## rating      -0.009546455 0.001271502 -7.508011 9.02122e-14
## 
## $tables[[5]]
##                Estimate  Std. Error   t value      Pr(>|t|)
## (Intercept) -0.07786467 0.006410316 -12.14678  8.467719e-33
## rating       0.05446072 0.001760453  30.93563 8.679221e-172
## 
## $tables[[6]]
##                Estimate  Std. Error   t value     Pr(>|t|)
## (Intercept) -0.02246572 0.006929757 -3.241921 1.206973e-03
## rating       0.02552663 0.001957900 13.037758 2.445904e-37
## 
## $tables[[7]]
##                 Estimate  Std. Error  t value     Pr(>|t|)
## (Intercept)  0.105320875 0.009524416 11.05799 1.264847e-27
## rating      -0.002670475 0.002478469 -1.07747 2.814015e-01
## 
## $tables[[8]]
##                Estimate  Std. Error  t value     Pr(>|t|)
## (Intercept) 0.043790583 0.007415128 5.905573 4.125071e-09
## rating      0.009437883 0.001975430 4.777634 1.903927e-06
## 
## $tables[[9]]
##               Estimate  Std. Error   t value      Pr(>|t|)
## (Intercept)  0.4951959 0.011520281  42.98470 8.443565e-286
## rating      -0.1043813 0.002757979 -37.84703 2.275448e-236
## 
## $tables[[10]]
##                  Estimate  Std. Error    t value     Pr(>|t|)
## (Intercept)  0.0863797171 0.006771871 12.7556657 7.157241e-36
## rating      -0.0004901109 0.001725167 -0.2840948 7.763674e-01

plot.estimateEffect(predict_topics_NP, model= model_NP_10Prrateby_Sent, cov.value1="pos",
 cov.value2="neg", covariate="rating",
 method="difference",
 nsims = 100,
 xlab="Negative Rating ....................................................Positive Rating", labeltype="custom", custom.labels=topic_labels_rating,ci.level=.99)

As could be seen, Service and Staff are associated with higher ratings, while Food in general - with lower. Predictably, Negative Experience topic has the most negative rating.

#model of rating + time
model_NP_10_1Prrateby_year<-stm(docs, vocab, prevalence=~rating_group+time_ym, K=10, data=meta, init.type = "Spectral", verbose=FALSE)
labelTopics(model_NP_10_1Prrateby_year)

## Topic 1 Top Words:
##       Highest Prob: good, food, servic, ambienc, great, time, also 
##       FREX: good, ambienc, great, servic, food, worth, atmospher 
##       Lift: atmospher, awsom, desir, fellow, good, raza, stater 
##       Score: good, servic, great, ambienc, food, time, also 
## Topic 2 Top Words:
##       Highest Prob: order, chicken, tast, biryani, rice, quantiti, paneer 
##       FREX: biryani, quantiti, cook, egg, biriyani, shawarma, wrap 
##       Lift: bawarchi, birayani, cauliflow, execut, gari, gravi, individu 
##       Score: chicken, biryani, order, quantiti, rice, piec, mutton 
## Topic 3 Top Words:
##       Highest Prob: place, nice, realli, awesom, can, music, experi 
##       FREX: floor, nice, music, awesom, crowd, place, night 
##       Lift: caution, girl, hindi, ladi, met, often, pub 
##       Score: nice, place, music, awesom, danc, hangout, floor 
## Topic 4 Top Words:
##       Highest Prob: like, restaur, tri, dish, one, much, littl 
##       FREX: expect, satisfi, rate, didnt, ala, briyani, littl 
##       Lift: ala, asia, briyani, congest, consider, crowdi, dosa 
##       Score: restaur, expect, dish, rate, like, briyani, zomato 
## Topic 5 Top Words:
##       Highest Prob: visit, friend, staff, love, best, amaz, will 
##       FREX: thank, attent, polit, birthday, celebr, hospit, courteous 
##       Lift: -hous, bdub, bewar, bike, celebr, contrast, crew 
##       Score: staff, amaz, love, friend, thank, excel, parti 
## Topic 6 Top Words:
##       Highest Prob: chicken, tri, place, fish, kebab, best, perfect 
##       FREX: kebab, mandi, bbq, mojito, yummi, grill, arabian 
##       Lift: alfredo, arab, bagel, bbq, click, cutleri, daawat 
##       Score: chicken, mandi, kebab, arabian, fish, marin, tikka 
## Topic 7 Top Words:
##       Highest Prob: deliveri, tri, chocol, cake, burger, like, paratha 
##       FREX: deliveri, cake, paratha, browni, coffe, fast, outlet 
##       Lift: blueberri, baker, banana, berri, bite, bought, boy 
##       Score: deliveri, fast, chocol, cake, paratha, browni, coffe 
## Topic 8 Top Words:
##       Highest Prob: chees, noodl, sauc, realli, chilli, tast, well 
##       FREX: chees, chines, indian, momo, north, pork, punjabi 
##       Lift: amritsari, beef, bhatur, chines, chole, compliment, copi 
##       Score: chees, momo, noodl, pork, chines, chilli, sauc 
## Topic 9 Top Words:
##       Highest Prob: order, food, bad, even, time, worst, will 
##       FREX: bad, worst, bill, pathet, min, horribl, smell 
##       Lift: arrog, burst, cancel, cheat, cleanli, cockroach, condition 
##       Score: worst, bad, pathet, order, wast, bill, min 
## Topic 10 Top Words:
##       Highest Prob: veg, starter, buffet, price, overal, main, lunch 
##       FREX: buffet, non, vegetarian, veg, desert, decent, lunch 
##       Lift: addit, affair, avg, bore, construct, desert, econom 
##       Score: veg, starter, buffet, main, cours, non, spread

topicprop15_1<- model_NP_10_1Prrateby_year$theta

predict_topics_1_NP <- estimateEffect(
  ~ rating_group + time_ym,
  stmobj   = model_NP_10_1Prrateby_year,
  metadata = meta,
  uncertainty = "Global"
)

effect15_pos <- extract.estimateEffect(
  x = predict_topics_1_NP,
  covariate = "time_ym",
  moderator = "rating_group",
  moderator.value = "pos"
)

effect15_pos <- extract.estimateEffect(
  x = predict_topics_1_NP,
  covariate = "time_ym",
  moderator = "rating_group",
  moderator.value = "neg"
)

pdf(
  file = "STM_topics_time_by_rating.pdf",
  width = 14,
  height = 10
)

K <- 10

for (topic_id in 1:K) {

  # Extract effects for NEG and POS
  effect <- lapply(c("neg", "pos"), function(i) {
    extract.estimateEffect(
      x = predict_topics_1_NP,
      covariate = "time_ym",
      method = "continuous",
      model = model_NP_10_1Prrateby_year,
      labeltype = "prob",
      topic = topic_id,
      n = 7,
      ci.level = 0.95,
      moderator = "rating_group",
      moderator.value = i
    )
  })

  effect <- do.call("rbind", effect)

  # Build plot
  p <- ggplot(
    effect,
    aes(
      x = covariate.value,
      y = estimate,
      ymin = ci.lower,
      ymax = ci.upper,
      group = moderator.value,
      fill = factor(moderator.value)
    )
  ) +
    geom_ribbon(alpha = 0.5) +
    geom_line(size = 1) +
    facet_wrap(~ label, nrow = 2) +
    labs(
      title = paste0(
        "Topic ", topic_id, ": ",
        topic_labels_rating[topic_id]
      ),
      x = "Year",
      y = "Expected Topic Proportion",
      fill = "Review"
    ) +
    theme_minimal(base_size = 14) +
    theme(
      legend.position = "bottom",
      plot.title = element_text(face = "bold", size = 16)
    )

  print(p)
}

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

dev.off()

## png 
##   2

#rating and place model
model_NP_10_2Prrateby<-stm(docs,vocab, prevalence=~rating_group+our_place, K=10, data=meta
, init.type = "Spectral", verbose=FALSE)
labelTopics(model_NP_10_2Prrateby)

## Topic 1 Top Words:
##       Highest Prob: good, food, servic, ambienc, great, time, also 
##       FREX: good, great, ambienc, servic, food, worth, atmospher 
##       Lift: atmospher, awsom, concentr, desir, fellow, hasubul, jayanta 
##       Score: good, great, servic, ambienc, food, time, also 
## Topic 2 Top Words:
##       Highest Prob: order, chicken, tast, biryani, rice, quantiti, paneer 
##       FREX: biryani, quantiti, butter, egg, shawarma, wrap, gravi 
##       Lift: bawarchi, birayani, bland, cauliflow, gari, gravi, individu 
##       Score: chicken, biryani, order, quantiti, rice, piec, mutton 
## Topic 3 Top Words:
##       Highest Prob: place, nice, awesom, realli, music, can, night 
##       FREX: music, floor, nice, awesom, danc, parti, crowd 
##       Lift: bare, caution, girl, hindi, often, accommod, depend 
##       Score: nice, place, awesom, music, danc, parti, hangout 
## Topic 4 Top Words:
##       Highest Prob: like, restaur, dish, littl, tri, didnt, much 
##       FREX: expect, satisfi, didnt, littl, need, ala, briyani 
##       Lift: asia, dosa, nonveg, penn, tofu, urban, ala 
##       Score: restaur, expect, like, dish, rate, zomato, briyani 
## Topic 5 Top Words:
##       Highest Prob: visit, friend, staff, love, best, amaz, will 
##       FREX: excel, thank, hospit, birthday, celebr, courteous, polit 
##       Lift: -hous, assist, bdub, celebr, crew, explain, game 
##       Score: staff, friend, amaz, love, thank, excel, visit 
## Topic 6 Top Words:
##       Highest Prob: chicken, fish, tikka, place, soup, kebab, mandi 
##       FREX: soup, kebab, mandi, wing, fish, bbq, marin 
##       Lift: apollo, arab, bbq, click, cutleri, daawat, deep 
##       Score: chicken, kebab, mandi, fish, tikka, marin, arabian 
## Topic 7 Top Words:
##       Highest Prob: deliveri, chocol, cake, deliv, never, paratha, time 
##       FREX: deliveri, cake, paratha, browni, coffe, fast, hotel 
##       Lift: blueberri, insist, cake, admit, alphonso, atoneplaceonlinecom, bcz 
##       Score: fast, deliveri, chocol, cake, paratha, deliv, browni 
## Topic 8 Top Words:
##       Highest Prob: tri, one, must, love, place, tast, best 
##       FREX: chees, chines, momo, north, pork, combo, favorit 
##       Lift: beat, beef, bhatur, bike, cheesi, compliment, copi 
##       Score: momo, chees, must, perfect, love, chines, north 
## Topic 9 Top Words:
##       Highest Prob: order, food, bad, even, time, servic, worst 
##       FREX: bad, worst, bill, pathet, min, poor, horribl 
##       Lift: anymor, arriv, arrog, bad, bouncer, burst, cancel 
##       Score: worst, bad, pathet, order, bill, wast, horribl 
## Topic 10 Top Words:
##       Highest Prob: veg, starter, buffet, overal, main, cours, decent 
##       FREX: buffet, burger, vegetarian, desert, decent, donut, limit 
##       Lift: add, addit, affair, apologis, avg, bengali, bore 
##       Score: veg, buffet, main, starter, cours, burger, non

topicprop15_2<-model_NP_10_2Prrateby$theta

predict_topics_2_NP<-estimateEffect(formula = ~rating_group+our_place, stmobj = model_NP_10_2Prrateby, metadata = prep$meta, uncertainty = "Global")

plot.estimateEffect(predict_topics_2_NP, model=model_NP_10_2Prrateby, cov.value1="Competitor",
 cov.value2="Our", covariate="our_place",
 method="difference",
 nsims = 100,
 xlab="Competitor.............................................................. Our",
 labeltype="custom", custom.labels=topic_labels_rating,ci.level=.99)

pdf(
  file = "STM_topics_place_by_rating_lines.pdf",
  width = 14,
  height = 10
)

K <- 10

for (topic_id in 1:K) {

  # Extract effects for NEG and POS
  effect <- lapply(c("neg", "pos"), function(i) {
    extract.estimateEffect(
      x = predict_topics_2_NP,
      covariate = "our_place",
      moderator = "rating_group",
      moderator.value = i,
      model = model_NP_10_2Prrateby,
      method = "pointestimate",
      topic = topic_id,
      ci.level = 0.95
    )
  })

  effect <- do.call("rbind", effect)

  # IMPORTANT: ensure numeric x for line/ribbon plotting
  effect$covariate.value <- as.numeric(effect$covariate.value)

  p <- ggplot(
    effect,
    aes(
      x = covariate.value,
      y = estimate,
      ymin = ci.lower,
      ymax = ci.upper,
      group = moderator.value,
      fill = factor(moderator.value)
    )
  ) +
    geom_ribbon(alpha = 0.5) +
    geom_line(size = 1) +
    scale_x_continuous(
      breaks = c(0, 1),
      labels = c(
        "0" = "Other places",
        "1" = "Our place"
      )
    ) +
    labs(
      title = paste0(
        "Topic ", topic_id, ": ",
        topic_labels_rating[topic_id]
      ),
      x = "Place type",
      y = "Expected Topic Proportion",
      fill = "Review"
    ) +
    theme_minimal(base_size = 14) +
    theme(
      legend.position = "bottom",
      plot.title = element_text(face = "bold", size = 16)
    )

  print(p)
}

dev.off()

## png 
##   2

model_NP_15_3Prrateby<-stm(docs,vocab, prevalence=~rating+our_place, K=10, data=meta
, init.type = "Spectral", content=~our_place, verbose=FALSE)
labelTopics(model_NP_15_3Prrateby)

## Topic Words:
##  Topic 1: great, vibe, ambienc, rock, good, courteous, especi 
##  Topic 2: dum, pulao, term, piec, spici, curri, quantiti 
##  Topic 3: parti, nice, night, accommod, awesom, shivam, clock 
##  Topic 4: sad, sweet, may, restaur, dish, deni, read 
##  Topic 5: thank, love, amaz, charm, visit, most, wow 
##  Topic 6: yummi, etc, membership, sky, roof, welcom, stick 
##  Topic 7: bill, els, refund, wrong, cream, water, anyon 
##  Topic 8: beer, chees, lasagn, vepudu, probabl, bruschetta, terminus 
##  Topic 9: rude, wast, behav, worst, pathet, said, horribl 
##  Topic 10: manchurian, cours, option, veg, main, price, non 
##  
##  Covariate Words:
##  Group 0: although, delici, soup, perfect, tri, lunch, apt 
##  Group 1: frio, vintag, background, spirit, everybodi, giveaway, jalebi 
##  
##  Topic-Covariate Interactions:
##  Topic 1, Group 0: govind, forward, servic, outstand, excel, breakfast, especi 
##  Topic 1, Group 1: ragi, ali, bengali, pulav 
##  
##  Topic 2, Group 0: shawarma, egg, noodl, meat, daal, oili, coat 
##  Topic 2, Group 1: junnu, natu, kodi, wine 
##  
##  Topic 3, Group 0: santosh, cascad, music, beef, north, find, fantast 
##  Topic 3, Group 1: forest, retro 
##  
##  Topic 4, Group 0: briyani, asian, chines, otherwis, zomato, big, pan 
##  Topic 4, Group 1: merri, gari, raju, island, ice, pita 
##  
##  Topic 5, Group 0: parantha, ipl, yum, ’ve, patient, enjoy, love 
##  Topic 5, Group 1: excel, servic, someth 
##  
##  Topic 6, Group 0: arabian, mandi, pastri, grill, tikka, style, best 
##  Topic 6, Group 1: mazzo, pool, antiqu, speaker, andhra, mild, bamboo 
##  
##  Topic 7, Group 0: paratha, chocol, browni, ice, pop, paid, deli 
##  Topic 7, Group 1: bare, shape, host, tiramisu, genuin, steward, disast 
##  
##  Topic 8, Group 0: momo, steam, punjabi, combo, dal, realli, world 
##  Topic 8, Group 1: tiki, shack, temptey, munch, music, liit, brilliant 
##  
##  Topic 9, Group 0: maggi, instead, store, replac, don’t, sent, irrit 
##  Topic 9, Group 1: berri, caramel, browni, minti, cherri, wedg, poster 
##  
##  Topic 10, Group 0: donut, paneer, fine, kebab, discount, burger, halwa 
##  Topic 10, Group 1: kritunga, ulavacharu, lost, telugu, aesthet, karachi, updat 
## 

pdf(
  file = "STM_perspectives_all_topics_custom_labels.pdf",
  width = 12,
  height = 9
)

K <- 10

for (topic_id in 1:K) {

  topic_label <- topic_labels_rating[topic_id]


  plot(
    model_NP_15_3Prrateby,
    type = "perspectives",
    topics = topic_id,
    main = paste0(
      "Topic ", topic_id, ": ",
      topic_label
    )
  )
}

# Close PDF
dev.off()

## png 
##   2

Conclusions: Topics of the restaurant reviews are mainly represented by food(as a whole and different categories of dishes), staff and service, night time spending. Other topics are rather generic and represent mainly positive - negative sentiment gained from visiting experience. Overall, service and staff tend to receive more positive rating while food topics are graded more modestly. Judging from the time perspective proportion of food topics usually tends to decrease while topics associated with service appear more frequently. Inspecting differences between competitors and our restaurants, topic concerning night time and main dishes appear more frequently and are usually rated positively. BBQ dishes topic appear less frequently. Positive words appear in topic Night Time more frequently among reviews of competitors, topic Main Dishes puts more focus on food among reviews of competitors.

`