quanteda - Quantitative Analysis of Textual Data

A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.

Last updated 15 days ago

corpusnatural-language-processingquantedatext-analytics

839 stars 9.28 score 16 dependencies 48 dependents

stopwords - Multilingual Stopword Lists

Provides multiple sources of stopwords, for use in text analysis and natural language processing.

Last updated 3 years ago

text-analysis

113 stars 6.16 score 1 dependencies 58 dependents

spacyr - Wrapper to the 'spaCy' 'NLP' Library

An R wrapper to the 'Python' 'spaCy' 'NLP' library, from <https://spacy.io>.

Last updated 4 months ago

extract-entitiesnlpspacyspeech-tagging

249 stars 6.00 score 13 dependencies 5 dependents

readtext - Import and Handling for Plain and Formatted Text Files

Functions for importing and handling text files and formatted text files with additional meta-data, such including '.csv', '.tab', '.json', '.xml', '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.

Last updated 7 months ago

encodingquantedatext

118 stars 4.73 score 46 dependencies 3 dependents

quanteda.textmodels - Scaling Models and Classifiers for Textual Data

Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, 'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish' model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.

Last updated 14 days ago

42 stars 4.02 score 28 dependencies

quanteda.textstats - Textual Statistics for the Quantitative Analysis of Textual Data

Textual statistics functions formerly in the 'quanteda' package. Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity. These functions extend the 'quanteda' package and are specially designed for sparse textual data.

Last updated 12 days ago

14 stars 3.95 score 20 dependencies 8 dependents

quanteda.textplots - Plots for the Quantitative Analysis of Textual Data

Plotting functions for visualising textual data. Extends 'quanteda' and related packages with plot methods designed specifically for text data, textual statistics, and models fit to textual data. Plot types include word clouds, lexical dispersion plots, scaling plots, network visualisations, and word 'keyness' plots.

Last updated 20 days ago

6 stars 3.17 score 48 dependencies

nsyllable - Count Syllables in Character Vectors

Counts syllables in character vectors for English words. Imputes syllables as the number of vowel sequences for words not found.

Last updated 3 years ago

9 stars 2.73 score 0 dependencies 9 dependents