{"name":"quanteda-package","title":"An R package for the quantitative analysis of textual data","aliases":["quanteda","quanteda-package"],"keywords":[],"usage":[],"arguments":[],"author":"\nKen Benoit and Paul Nulty\n\nA set of functions for creating and managing text corpora,\n extracting features from text corpora, and analyzing those features using\n quantitative methods.\n \n quanteda makes it easy to manage texts in the form of a corpus,\n defined as a collection of texts that includes document-level variables\n specific to each text, as well as meta-data for documents and for the\n collection as a whole. quanteda includes tools to make it easy and\n fast to manuipulate the texts in a corpus, by performing the most common\n natural language processing tasks simply and quickly, such as tokenizing,\n stemming, or forming ngrams. quanteda's functions for tokenizing\n texts and forming multiple tokenized documents into a document-feature\n matrix are both extremely fast and extremely simple to use.\n quanteda can segment texts easily by words, paragraphs, sentences,\n or even user-supplied delimiters and tags.\n \n Built on the text processing functions in the stringi package, which\n is in turn built on C++ implementation of the ICU libraries for Unicode\n text handling, quanteda pays special attention to fast and correct\n implementation of Unicode and the handling of text in any character set.\n \n quanteda is built for efficiency and speed, through its design\n around three infrastructures: the stringi package for text\n processing, the data.table package for indexing large documents\n efficiently, and the Matrix package for sparse matrix objects. If you\n can fit it into memory, quanteda will handle it quickly. (And\n eventually, we will make it possible to process objects even larger than\n available memory.)\n \n quanteda is principally designed to allow users a fast and\n convenient method to go from a corpus of texts to a selected matrix of\n documents by features, after defining what the documents and features. The\n package makes it easy to redefine documents, for instance by splitting them\n into sentences or paragraphs, or by tags, as well as to group them into\n larger documents by document variables, or to subset them based on logical\n conditions or combinations of document variables. The package also\n implements common NLP feature selection functions, such as removing\n stopwords and stemming in numerous languages, selecting words found in\n dictionaries, treating words as equivalent based on a user-defined\n \"thesaurus\", and trimming and weighting features based on document\n frequency, feature frequency, and related measures such as tf-idf.\n \n Once constructed, a quanteda \"dfm\"\" can be easily analyzed using\n either quanteda's built-in tools for scaling document positions,\n or used with a number of other text analytic tools, such as: topic models\n (including converters for direct use with the topicmodels, LDA, and stm\n packages) document scaling (using quanteda's own functions for the\n \"wordfish\" and \"Wordscores\" models, direct use with the ca package for\n correspondence analysis, or scaling with the austin package) machine\n learning through a variety of other packages that take matrix or\n matrix-like inputs.\n \n Additional features of quanteda include:

the ability to explore texts using key-words-in-context;\n
fast computation of a variety of readability indexes;\n
fast computation of a variety of lexical diversity measures;\n
quick computation of word or document similarities, for clustering or to compute distances for other purposes; and\n
a comprehensive suite of descriptive statistics on text such as the number of sentences, words, characters, or\n syllables per document.\n

\n\n","seealso":[],"examples":[],"description":"\nAn R package for the quantitative analysis of textual data\n","details":[],"value":[],"references":[],"note":[],"sections":[],"package":{"package":"quanteda","version":"0.9.8.5"}}

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