Text Mining With R -

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.

# Using bing lexicon (positive/negative) bing_sent <- get_sentiments("bing") sentiment_scores <- cleaned_austen %>% inner_join(bing_sent, by = "word") %>% count(book = austen_books()$book, sentiment) %>% # approximate pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>% mutate(net_sentiment = positive - negative)

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.

word_counts <- cleaned_austen %>% count(word, sort = TRUE) word_counts %>% head(10)