%!s(int=2026) © %!d(string=Solar Index)
graph LR A[Raw Text] --> B[Preprocessing] --> C[Tokenization] --> D[Stop Word Removal] --> E[Analysis] --> F[Visualization] library(tidyverse) library(tidytext) library(janeaustenr) Load sample text (Jane Austen's books) austen_books <- austen_books() head(austen_books) 3.2. Preprocessing & Tokenization Tokenization splits text into meaningful units (words, sentences, n-grams). tidytext uses unnest_tokens() .
data(stop_words) cleaned_austen <- tidy_austen %>% anti_join(stop_words, by = "word") Count most common words:
is an exceptional language for text mining. With a rich ecosystem of packages—most notably the tidytext , quanteda , and tm frameworks—R allows analysts to clean, tokenize, analyze sentiment, model topics, and visualize textual patterns efficiently.
word_counts %>% filter(n > 500) %>% ggplot(aes(x = reorder(word, n), y = n)) + geom_col(fill = "steelblue") + coord_flip() + labs(title = "Most Frequent Words in Jane Austen's Novels", x = "Word", y = "Count") + theme_minimal() Sentiment lexicons (e.g., AFINN , bing , nrc ) assign emotional valence to words.
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.
graph LR A[Raw Text] --> B[Preprocessing] --> C[Tokenization] --> D[Stop Word Removal] --> E[Analysis] --> F[Visualization] library(tidyverse) library(tidytext) library(janeaustenr) Load sample text (Jane Austen's books) austen_books <- austen_books() head(austen_books) 3.2. Preprocessing & Tokenization Tokenization splits text into meaningful units (words, sentences, n-grams). tidytext uses unnest_tokens() .
data(stop_words) cleaned_austen <- tidy_austen %>% anti_join(stop_words, by = "word") Count most common words: Text Mining With R
is an exceptional language for text mining. With a rich ecosystem of packages—most notably the tidytext , quanteda , and tm frameworks—R allows analysts to clean, tokenize, analyze sentiment, model topics, and visualize textual patterns efficiently. sentiment_scores library(wordcloud) word_counts %>
word_counts %>% filter(n > 500) %>% ggplot(aes(x = reorder(word, n), y = n)) + geom_col(fill = "steelblue") + coord_flip() + labs(title = "Most Frequent Words in Jane Austen's Novels", x = "Word", y = "Count") + theme_minimal() Sentiment lexicons (e.g., AFINN , bing , nrc ) assign emotional valence to words. max.words = 100
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.