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rocket-league-review-sentiment-analysis.R
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rocket-league-review-sentiment-analysis.R
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library(dplyr)
library(rvest)
library(tm)
# page 1
link = "https://www.trustpilot.com/review/rocketleague.com"
page = read_html(link)
review = page %>% html_nodes(".styles_reviewContent__0Q2Tg") %>% html_text()
date = page %>% html_nodes("time") %>% html_text()
user = page %>% html_nodes(".styles_consumerName__dP8Um") %>% html_text()
df = data.frame(date, user, review)
# page 2
link = "https://www.trustpilot.com/review/rocketleague.com?page=2"
page = read_html(link)
review = page %>% html_nodes(".styles_reviewContent__0Q2Tg") %>% html_text()
date = page %>% html_nodes("time") %>% html_text()
user = page %>% html_nodes(".styles_consumerName__dP8Um") %>% html_text()
df2 = data.frame(date, user, review)
# page 3
link = "https://www.trustpilot.com/review/rocketleague.com?page=3"
page = read_html(link)
review = page %>% html_nodes(".styles_reviewContent__0Q2Tg") %>% html_text()
date = page %>% html_nodes("time") %>% html_text()
user = page %>% html_nodes(".styles_consumerName__dP8Um") %>% html_text()
df3 = data.frame(date, user, review)
# combine data frames
df_com <- df %>%
bind_rows(df2)
df_com <- df_com %>%
bind_rows(df3)
df_com
# start text analysis
corpus <- iconv(df_com$review)
corpus <- Corpus(VectorSource(corpus))
inspect(corpus)
corpus <- tm_map(corpus, tolower)
inspect(corpus)
corpus <- tm_map(corpus, removePunctuation)
inspect(corpus)
corpus <- tm_map(corpus, removeNumbers)
inspect(corpus)
cleanset <- tm_map(corpus, removeWords, stopwords('english'))
inspect(corpus)
cleanset <- tm_map(cleanset, removeWords, c('aapl', 'apple'))
cleanset <- tm_map(cleanset, gsub,
pattern = 'stocks',
replacement = 'stock')
cleanset <- tm_map(cleanset, stemDocument)
cleanset <- tm_map(cleanset, stripWhitespace)
inspect(cleanset[1:5])
# analysis
library(tidyverse)
library(tidytext)
library(RColorBrewer)
db_new <- unnest_tokens(tbl = df_com, input = review, output = word)
stp_wrds <- get_stopwords(source = "smart")
db_new <- anti_join(db_new, stp_wrds, by = "word")
bing <- get_sentiments(lexicon = "bing")
db_bing <- inner_join(db_new, bing, by = "word")
db_bing <- count(db_bing, date, user, word, sentiment)
db_bing <- spread(key = sentiment, value = n, fill = 0, data = db_bing)
db_bing <- mutate(sentiment = positive - negative, .data = db_bing)
mean(db_bing$sentiment, na.rm = TRUE)
ggplot(mapping = aes(x = user, y = sentiment, fill = sentiment)) +
geom_col()
sentiment_by_user <- db_bing %>%
group_by(user) %>%
summarize(user_sentiment = sum(sentiment)) %>%
arrange(user_sentiment) %>%
mutate(Value = case_when(user_sentiment > 0 ~ 'positive',
user_sentiment == 0 ~ 'neutral',
user_sentiment < 0 ~ 'negative')) %>%
filter(Value == 'positive' | Value == 'negative')
sentiment_by_user %>% summarize(mean(user_sentiment))
sentiment_by_user %>% ggplot(aes(x = user, y = user_sentiment, fill = Value)) +
geom_col() + theme_classic() + geom_hline(yintercept = 0, alpha = .7) +
labs(x = "User Reviews", y = "Sentiment Value", title = "Rocket League Reviews Sentiment Analysis") + geom_hline(yintercept = -2.68, linetype = 3) + geom_label(aes(x = 33, y = -2.68,label = 'Mean Sentiment'), fill = "#f4f1de", show.legend = FALSE) +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), legend.position = "right")
db_bing %>%
ggplot(aes(x = user, y = sentiment, fill = sentiment)) +
geom_col(position = "stack")
library(widyr) # for pairwise correlation
library(igraph) # for data visualization
library(ggraph) # for data visualization
review_words <- df_com %>%
unnest_tokens(output = word, input = review) %>%
anti_join(stop_words, by = "word") %>%
filter(str_detect(word, "[:alpha:]")) %>%
distinct()
reviews_that_mention_word <- review_words %>%
count(word, name = "number_of_reviews") %>%
filter(number_of_reviews >= 7)
review_correlations <- review_words %>%
semi_join(reviews_that_mention_word, by = "word") %>%
pairwise_cor(item = word, feature = user) %>%
filter(correlation >= 0.01)
graph_from_data_frame(d = review_correlations,
vertices = reviews_that_mention_word %>%
semi_join(review_correlations, by = c("word" = "item1"))) %>%
ggraph(layout = "fr") +
geom_edge_link(aes(alpha = correlation)) +
geom_node_point() +
geom_node_text(aes(color = number_of_reviews, label = name), repel = TRUE, check_overlap = TRUE, size = 4) +
labs(title = "Rocket League Reviews") +
scale_colour_gradientn(colours=c("#3e3b92", "#f44369")) +
theme(panel.background = element_rect(fill = "#eaeaea"),
plot.background = element_rect(fill = "#ffffff"))