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yelp  <- readRDS("C:\\Users\\Xiayang Xiao\\Desktop\\yelp.RDS")
yelptexts <- yelp$text
stars <- yelp$stars
#iconv(x, "latin1", "ASCII", sub="")
clean.text = function(x)
{
  
 
  x = gsub("rt", "", x)
  
  x = gsub("@\\w+", "", x)
 
  x = gsub("[[:punct:]]", "", x)
  x = gsub("[[:digit:]]", "", x)
  x = gsub("http\\w+", "", x)
  x = gsub("[ |\t]{2,}", "", x)
  x = gsub("^ ", "", x)
  x = gsub(" $", "", x)
  
  x = tolower(x)
  return(x)
}
yelp = clean.text(yelptexts)
services = scan('C:\\Users\\Xiayang Xiao\\Desktop\\service.txt', what='character', comment.char=';')

Read 328 items

price = scan('C:\\Users\\Xiayang Xiao\\Desktop\\price.txt', what='character', comment.char=';')

Read 577 items

food = scan('C:\\Users\\Xiayang Xiao\\Desktop\\food.txt', what='character', comment.char=';')

Read 363 items

score.topic = function(sentences, dict, stars, .progress='none')
{
  require(plyr)
  require(stringr)
  require(stringi)
  
  
  scores = laply(sentences, function(sentence, dict, star) {
    
    
sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    sentence = tolower(sentence)    
   
    word.list = str_split(sentence, '\\s+')
   
    words = unlist(word.list)
    
    star = unlist(star)
    
    
    topic.matches = match(words, dict)
    
    
    topic.matches = !is.na(topic.matches)
   
    score = sum(topic.matches)
    
    return(score)
  }, dict, stars, .progress=.progress )
  
  topicscores.df = data.frame(score=scores, text=sentences, star=stars)
  return(topicscores.df)
}
topic.scoresprice= score.topic(yelptexts, price, stars, .progress='none')
topic.scorefood = score.topic(yelptexts, food, stars, .progress='none')
topic.services = score.topic(yelptexts, services, stars, .progress='none')
score.price <- topic.scoresprice$score
score.food <- topic.scorefood$score
score.services <- topic.services$score
score <- cbind.data.frame(score.price, score.food, score.services,yelptexts,stars)
N= nrow(subset(score, stars==4))
scores.services = nrow(subset(score, score.services != 0 & stars == 4))
scores.food = nrow(subset(score, score.food != 0 & stars == 4))
scores.price = nrow(subset(score, score.price != 0 & stars == 4))
nomentioned = nrow(subset(score, score.price == 0 & score.food ==0 & score.services ==0 & stars == 4))
dftemp=data.frame(topic=c("price", "food","services","nomentioned"), 
                  number=c(scores.price,scores.food,scores.services, nomentioned))
library (plotly)

戼<U+3E33><U+033C>愼<U+3E64>戼<U+3E30>昼<U+3E63>愼<U+3E31>愼<U+3E65>plotly愼<U+3E31>愼<U+3E66>挼<U+3E61>挼<U+3E37>搼<U+3E33>挼<U+3E33>R戼<U+3E30>汾3.4.3 挼<U+3E30>戼<U+3E34>戼<U+3E64>愼<U+3E38>搼<U+3E34>攼<U+3E63>戼<U+3E35>挼<U+3E34>搼<U+3E34>搼<U+3E38>挼<U+3E38>攼<U+3E62>搼<U+3E30>攼<U+3E38><U+04AA>戼<U+3E35><U+0133><U+033C>愼<U+3E64>戼<U+3E30>昼<U+3E63>愼<U+3E33>戼<U+3E61>ggplot2
戼<U+3E33><U+033C>愼<U+3E64>戼<U+3E30>昼<U+3E63>愼<U+3E31>愼<U+3E65>ggplot2愼<U+3E31>愼<U+3E66>挼<U+3E61>挼<U+3E37>搼<U+3E33>挼<U+3E33>R戼<U+3E30>汾3.4.4 挼<U+3E30>戼<U+3E34>戼<U+3E64>愼<U+3E38>搼<U+3E34>攼<U+3E63>戼<U+3E35>挼<U+3E34>
搼<U+3E34>搼<U+3E38>挼<U+3E38>攼<U+3E62>戼<U+3E33><U+033C>愼<U+3E64>戼<U+3E30>昼<U+3E63>愼<U+3E33>戼<U+3E61>愼<U+3E31>愼<U+3E65>plotly愼<U+3E31>愼<U+3E66>

The following object is masked from 愼<U+3E31>愼<U+3E65>package:ggplot2愼<U+3E31>愼<U+3E66>:

    last_plot

The following objects are masked from 愼<U+3E31>愼<U+3E65>package:plyr愼<U+3E31>愼<U+3E66>:

    arrange, mutate, rename, summarise

The following object is masked from 愼<U+3E31>愼<U+3E65>package:stats愼<U+3E31>愼<U+3E66>:

    filter

The following object is masked from 愼<U+3E31>愼<U+3E65>package:graphics愼<U+3E31>愼<U+3E66>:

    layout

p <- plot_ly(dftemp, x = ~topic, y = ~number,  type = "bar")
p    

戼<U+3E33><U+033C>愼<U+3E64>戼<U+3E30>昼<U+3E63>愼<U+3E31>愼<U+3E65>bindrcpp愼<U+3E31>愼<U+3E66>挼<U+3E61>挼<U+3E37>搼<U+3E33>挼<U+3E33>R戼<U+3E30>汾3.4.4 挼<U+3E30>戼<U+3E34>戼<U+3E64>愼<U+3E38>搼<U+3E34>攼<U+3E63>戼<U+3E35>挼<U+3E34>

#pie chat
score <- cbind(score.price, score.food, score.services,yelptexts,stars) 
score.services.only = nrow(subset(score, score.services != 0 & score.food == 0 & score.price ==0 & stars == 5))
score.food.only = nrow(subset(score, score.food != 0 & score.price ==0 & score.services ==0 & stars == 5))
score.price.only = nrow(subset(score, score.price != 0 & score.services ==0 & score.food ==0 & stars == 5))
score.price.food = nrow(subset(score, score.food != 0 & score.price !=0 & score.services ==0 & stars == 5))
score.price.services = nrow(subset(score, score.price != 0 & score.services !=0 & score.food == 0 & stars == 5))
score.food.services = nrow(subset(score, score.price == 0 & score.services !=0 & score.food !=0 & stars == 5))
score.Trine = nrow(subset(score, score.services != 0 & score.food != 0 & score.price !=0 & stars == 5))
nomentioned = nrow(subset(score, score.price == 0 & score.food ==0 & score.services ==0 & stars == 5))
dftemp=data.frame(topic=c("price only", "food only","services only", " price and food","price and services","food and services" ,"Trine" , "nomentioned"), 
                  number=c(score.price.only,score.food.only,score.services.only,score.price.food,score.price.services,score.food.services,score.Trine, nomentioned))
library(plotly)
p <- plot_ly(data=dftemp, labels = ~topic, values = ~number, type = 'pie') %>%
  layout(title = 'Pie Chart of Yelp',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
p

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