![]() ![]() The Most Common positive and negative words in the reviews. The attitude may be a judgment or evaluation. hotel guest) with respect to his (or her) past experience or emotional reaction towards the hotel. In our case, we aim to determine the attitude of a reviewer (i.e. ![]() Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, for applications that range from marketing to customer service to clinical medicine. The conversation about service and food peaked at the beginning of the data around 2003, It has been in a downward trend after 2005, with occasional peaks. Service and food were both the top topics prior to 2010. We want to ask questions like: what words have been increasing in frequency over time in TripAdvisor reviews? reviews_per_month % group_by(month) %>% summarize(month_total = n()) word_month_counts % filter(word_total >= 1000) %>% count(word, month) %>% complete(word, month, fill = list(n = 0)) %>% inner_join(reviews_per_month, by = "month") %>% mutate(percent = n / month_total) %>% mutate(year = year(month) + yday(month) / 365) mod % nest(-word) %>% mutate(model = map(data, mod)) %>% unnest(map(model, tidy)) %>% filter(term = "year") %>% arrange(desc(estimate)) slopes %>% head(9) %>% inner_join(word_month_counts, by = "word") %>% mutate(word = reorder(word, -estimate)) %>% ggplot(aes(month, n / month_total, color = word)) + geom_line(show.legend = FALSE) + scale_y_continuous(labels = percent_format()) + facet_wrap(~ word, scales = "free_y") + expand_limits(y = 0) + labs(x = "Year", y = "Percentage of reviews containing this word", title = "9 fastest growing words in TripAdvisor reviews", subtitle = "Judged by growth rate over 15 years") What words and topics have become more frequent, or less frequent, over time? These could give us a sense of the hotel changing ecosystem, such as service, renovation, problem solving and let us predict what topics will continue to grow in relevance. The most common trigram is “hilton hawaiian village”, followed by “diamond head tower”, and so on. Trigramsīigrams sometimes are not enough, let’s see what are the most common trigrams in Hilton Hawaiian Village’s TripAdvisor reviews? review_trigrams % unnest_tokens(trigram, review_body, token = "ngrams", n = 3) trigrams_separated % separate(trigram, c("word1", "word2", "word3"), sep = " ") trigrams_filtered % filter(!word1 %in% stop_words$word) %>% filter(!word2 %in% stop_words$word) %>% filter(!word3 %in% stop_words$word) trigram_counts % count(word1, word2, word3, sort = TRUE) trigrams_united % unite(trigram, word1, word2, word3, sep = " ") trigrams_united %>% count(trigram, sort = TRUE) However, we do not see clear clustering structure in the network. The network graph shows strong connections between the top several words (“hawaiian”, “village”, “ocean” and “view”). The above visualizes the common bigrams in TripAdvisor reviews, showing those that occurred at least 1000 times and where neither word was a stop-word. Load the Libraries library(dplyr) library(readr) library(lubridate) library(ggplot2) library(tidytext) library(tidyverse) library(stringr) library(tidyr) library(scales) library(broom) library(purrr) library(widyr) library(igraph) library(ggraph) library(SnowballC) library(wordcloud) library(reshape2) theme_set(theme_minimal()) The Data df <- read_csv("Hilton_Hawaiian_Village_Waikiki_Beach_Resort-Honolulu_Oahu_Hawaii_en.csv") df <- df df$review_date <- as.Date(df$review_date, format = "%d-%B-%y") dim(df) min(df$review_date) max(df$review_date) I will not discuss the details of the web scraping, the Python code for the process can be found here. In an effort to more thoroughly understand whether hotel guests reviews influence hotel performance overtime, I scraped all English reviews from TripAdvisor for one hotel - Hilton Hawaiian Village. each of thousands of TripAdvisor review text, can be challenging. However, understanding the nuance of TripAdvisor bubble score vs. Study after study has shown that TripAdvisor is becoming terrifyingly important in a traveler’s decision making process. How to apply natural language processing to sort through hotel reviews
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