Package Loading

set.seed(12345)
options(digits = 3, scipen = 9999, width = 120, knitr.table.format = "rst")

pacman::p_load(tokenizers,stopwords,dplyr,ggplot2,
               ggthemes,tidytext, qdap, tm,lda,topicmodels, ggrepel,
               tidyverse,wordcloud,wordcloud2, skmeans, clue, cluster, knitr,
               fpc, ldatuning, wakefield)

library(SentimentAnalysis)
library(sentimentr)
library(widyr)
library(plotly)

pacman::p_load(tidyverse, tidytext, textclean, tokenizers, markovchain)
pacman::p_load(stm, rvest, tm)
pacman::p_load(gutenbergr)

library(FactoMineR)
library(factoextra)
library(scales)
library(magrittr)

1 Conneticut

Pulling data and turning it into text:

webpage_c = read_html("https://www.ctpost.com/news/coronavirus/article/Experts-Increased-restaurant-capacity-partly-to-16081210.php")

ct0  = webpage_c %>% html_nodes("p") %>% html_text()

ct1 = data.frame(text = ct0)

head(ct1,25)
nrow(ct1)

ct2 = ct1 %>% unnest_tokens(word, text, 
                              to_lower = T,
                              strip_punct = T,
                              strip_numeric = T
)

ct2 %>% count(word, sort = T)

ct2 = ct2 %>% anti_join(stop_words, by = "word")
head(ct2)

ct3 = cbind.data.frame(linenumber = row_number(ct2), ct2)
head(ct3)

ct3 %>% count(word, sort = T)

The plots show us that this article on Connecticut reopening restaurants contains many negative sentiments and consists of various emotions, including a lot of trust, a lot of positive, and a decent amount of joy and sadness. The fear sentiment plots are all postive.

2 New York

Pulling data and turning it into text:

webpage_c = read_html("https://qns.com/2021/03/new-york-city-restaurants-can-open-up-50-indoor-dining-capacity-starting-march-19-cuomo/")
ny0  = webpage_c %>% html_nodes("p") %>% html_text()

ny1 = data.frame(text = ny0)

head(ny1,17)
nrow(ny1)

ny2 <- ny1 %>% 
  slice(-(11:17))

head(ny2) 

ny3 = ny2 %>% unnest_tokens(word, text, 
                              to_lower = T,
                              strip_punct = T,
                              strip_numeric = T
)

ny3 %>% count(word, sort = T)

ny3 = ny3 %>% anti_join(stop_words, by = "word")
head(ny3)

ny4 = cbind.data.frame(linenumber = row_number(ny3), ny3)


ny4 %>% count(word, sort = T)

The plots show us that this article on New York reopening restaurants contains many negative sentiments and consists of various emotions, including a lot of positive and a decent amount of fear, anticipation, and trust. The fear sentiment plots are all postive.

3 New Jersey

Pulling data and turning it into text:

webpage_c = read_html("https://www.northjersey.com/story/news/2021/03/10/nj-indoor-dining-capacity-gov-phil-murphy-covid-restaurants-salons/4642606001/")
nj0  = webpage_c %>% html_nodes("p") %>% html_text()

nj1 = data.frame(text = nj0)

head(nj1,17)
nrow(nj1)

nj2 = nj1 %>% unnest_tokens(word, text, 
                            to_lower = T,
                            strip_punct = T,
                            strip_numeric = T
)

nj2 %>% count(word, sort = T)

nj2 = nj2 %>% anti_join(stop_words, by = "word")
head(nj2)

nj3 = cbind.data.frame(linenumber = row_number(nj2), nj2)
head(nj3)

nj3 %>% count(word, sort = T)

The plots show us that this article on New Jersey reopening restaurants contains many negative sentiments and consists of various emotions, including a lot of trust, a lot of positive, and a decent amount of joy, anticipation, and sadness. The fear sentiment plots are all postive.

4 Massachusetts

Pulling data and turning it into text:

webpage_c = read_html("https://thesuffolkjournal.com/33210/news/mass-restaurants-no-longer-have-capacity-limits/")
ma0  = webpage_c %>% html_nodes("p") %>% html_text()

ma1 = data.frame(text = ma0)

head(ma1,48)
nrow(ma1)

ma2 <- ma1 %>% 
  slice(-(18:48))%>%
  slice(-(1:4))

head(ma2) 

ma3 = ma2 %>% unnest_tokens(word, text, 
                            to_lower = T,
                            strip_punct = T,
                            strip_numeric = T
)

ma3 %>% count(word, sort = T)

ma3 = ma3 %>% anti_join(stop_words, by = "word")
head(ma3)

ma4 = cbind.data.frame(linenumber = row_number(ma3), ma3)
head(ma4)

ma4 %>% count(word, sort = T)

The plots show us that this article on Massachusetts reopening restaurants contains many negative sentiments and consists of various emotions, including a decent amount of positive, negative, fear, anticipation, and trust. The fear sentiment plots are all postive.

5 Rhode Island

Pulling data and turning it into text:

webpage_c = read_html("https://www.providencejournal.com/story/entertainment/dining/2021/04/07/allowing-outdoor-dining-recoup-financially-covid-pandemic/4828603001/")
ri0  = webpage_c %>% html_nodes("p") %>% html_text()

ri1 = data.frame(text = ri0)

head(ri1,24)
nrow(ri1)

ri2 = ri1 %>% unnest_tokens(word, text, 
                            to_lower = T,
                            strip_punct = T,
                            strip_numeric = T
)

ri2 %>% count(word, sort = T)

ri2 = ri2 %>% anti_join(stop_words, by = "word")
head(ri2)

ri3 = cbind.data.frame(linenumber = row_number(ri2), ri2)
head(ri3)

ri3 %>% count(word, sort = T)

The plots show us that this article on Rhode Islands reopening restaurants contains many negative sentiments and consists of various emotions, including a decent amount of positive, negative, fear, anticipation, and trust. The fear sentiment plots are all postive.

6 Comparison Plot

The comparison plot shows that most states have a variation in emotions, but each state has a high ranking in both positive and negative emotions. Positive being the highest.