TALL provides a graphical text-analysis environment for corpus exploration, linguistic annotation, topic models and sentiment.
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Tag: text mining
Syuzhet – sentiment analysis and narrative plot arcs
Syuzhet analyses sentiment and emotional movement in narratives with lexicon-based scores, emotion categories and plot arcs.
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text – transformer-based natural language analysis in R
text provides transformer-based text embeddings, semantic similarity, prediction and language analysis through R workflows.
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corpustools – manage, query and analyse text corpora
corpustools manages and explores tokenised corpora with search, corpus comparison, keyword-in-context views and semantic networks.
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textTinyR – text processing for small and large datasets
textTinyR provides text preprocessing, tokenisation and n-gram generation for large or small collections of documents in R.
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sentimentr – sentence-level sentiment analysis in R
sentimentr measures sentence-level sentiment while accounting for negation, intensifiers, downtoners and grouped polarity scores.
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topicmodels – latent Dirichlet allocation and topic modelling in R
topicmodels fits latent Dirichlet allocation and correlated topic models, then explores document-topic probabilities in R.
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tokenizers – fast and consistent text tokenisation in R
tokenizers converts text into words, sentences, n-grams, paragraphs and other token types with consistent functions for R analysis.
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textclean – clean and normalise textual data
textclean prepares noisy text for analysis with replacements for contractions, emoji, URLs, HTML, whitespace and other patterns.
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tidytext – text mining using tidy data principles
tidytext brings text mining into tidy R workflows with tokenisation, sentiment lexicons, term frequencies, and data visualisation.
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quanteda – quantitative analysis of textual data
quanteda provides powerful quantitative text analysis with corpus management, tokenisation, document-feature matrices, and dictionaries.
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tm – text mining infrastructure for R
tm offers an extensible R text-mining framework with document corpora, preprocessing tools, term-document matrices, and metadata.
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UDPipe – multilingual linguistic annotation in R
UDPipe provides multilingual tokenisation, part-of-speech tagging, lemmatisation, dependency parsing, and keyword extraction in R.
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Word Vectors – build and explore word embeddings in R
Word Vectors trains and explores word embeddings in R with word2vec models, nearest-neighbour searches and cosine similarity.
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spacyr – R interface to the spaCy NLP library
spacyr connects R to spaCy for tokenisation, part-of-speech tagging, lemmatisation, named entity recognition and dependency parsing.
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text2vec – text analysis and word embeddings in R
text2vec supports efficient text vectorisation, document-term matrices, word embeddings, similarity analysis, and topic modelling in R.
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MeTA – text retrieval and data analysis toolkit
MeTA combines text tokenization, indexing, classification, ranking, topic models and linguistic processing in one research toolkit.
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