Text Mining With R Page
tf_idf <- cleaned_austen %>% count(book, word) %>% bind_tf_idf(word, book, n) %>% arrange(desc(tf_idf)) tf_idf %>% group_by(book) %>% slice_max(tf_idf, n = 3) 4.1. N-grams (Pairs of Words) austen_bigrams <- austen_books() %>% unnest_tokens(bigram, text, token = "ngrams", n = 2) Count common bigrams bigram_counts <- austen_bigrams %>% separate(bigram, into = c("word1", "word2"), sep = " ") %>% filter(!word1 %in% stop_words$word) %>% filter(!word2 %in% stop_words$word) %>% count(word1, word2, sort = TRUE) 4.2. Topic Modeling (Latent Dirichlet Allocation) Using tidytext + topicmodels to discover hidden themes.
tidy_austen <- austen_books() %>% unnest_tokens(word, text) # one word per row tidy_austen Stop words (the, and, to, of) carry little meaning. tidytext provides get_stopwords() . Text Mining With R
1. Introduction In the age of big data, most information exists as unstructured text —emails, social media posts, reviews, news articles, and research papers. Unlike numerical data, text cannot be directly fed into a statistical model. Text mining (or text analytics) is the process of transforming this free-form text into structured, quantifiable data for analysis, pattern discovery, and prediction. Introduction In the age of big data, most
sentiment_scores library(wordcloud) word_counts %>% with(wordcloud(word, n, max.words = 100, colors = brewer.pal(8, "Dark2"))) 3.7. Term Frequency – Inverse Document Frequency (TF-IDF) TF-IDF identifies words that are important to a document within a corpus. sentiment_scores library(wordcloud) word_counts %>
with a bar chart:
