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An instance of this class wraps a collection of documents\nand provides several methods to interact with this collection and perform post-processing tasks such as stemming,\nstopword removal etc.\n\nA new corpus is created by calling the constructor\n\n``` javascript\nvar my_corpus = new tm.Corpus([]);\n```\n\nwhere `[]` is an array of text documents which form the data of the corpus. The class supports method chaining, such that mutliple methods can be invoked after each other, e.g.\n\n``` javascript\nmy_corpus\n\t.trim()\n\t.toLower()\n```\n\nThe following methods and properties are part of the Corpus class:\n\n### Methods\n\n#### `.addDoc(doc)`\n\nAdd a single document to the corpus. Has to be a string.\n\n#### `.addDocs(docs)`\n\nAdds a collection of documents (in form of an array of strings) to the corpus.\n\n#### `.clean()`\nStrips extra whitespace from all documents, leaving only at most one whitespace between any two other characters.\n\n#### `.map(fun)`\nApplies the function supplied to `fun` to each document in the corpus and maps each document to the result of its respective\nfunction call.\n\n#### `.removeInterpunctuation()`\nRemoves interpunctuation characters (! ? . , ; -) from all documents.\n\n#### `.removeNewlines()`\nRemoves newline characters (\\n) from all documents.\n\n#### `.removeWords(words[, case_insensitive])`\nRemoves all words in the supplied `words` array from all documents. This function is usually invoked to remove stopwords. For convenience,\nthe *text-miner* package ships with a list of stopwords for different languages. These are stored in the\n`STOPWORDS` object of the module.\n\nCurrently, stopwords for the following languages are included:\n\n``` javascript\nSTOPWORDS.DE\nSTOPWORDS.EN\nSTOPWORDS.ES\nSTOPWORDS.IT\n```\n\nAs a concrete example, we could remove all english stopwords from corpus `my_corpus` as follows:\n\n``` javascript\nmy_corpus.removeWords( tm.STOPWORDS.EN )\n```\n\nThe second (optional) parameter of the function `case_insensitive` expects a Boolean indicating whether to ignore cases or not.\nThe default value is `false`.\n\n#### `.removeDigits()`\n\nRemoves any digits occuring in the texts.\n\n#### `.removeInvalidCharacters()`\n\nRemoves all characters which are unknown or unrepresentable in Unicode.\n\n#### `.stem(type)`\nPerforms stemming of the words in each document. Two stemmers are supported: Porter and Lancaster. The former is the default\noption. Passing \"Lancaster\" to the `type` parameter of the function ensured that the latter one is used.\n\n#### `.toLower()`\nConverts all characters in the documents to lower-case.\n\n#### `.toUpper()`\nConverts all characters in the documents to upper-case.\n\n#### `.trim()`\nStrips off whitespace at the beginning and end of each document.\n\n## DocumentTermMatrix / TermDocumentMatrix\n\nWe can pass a corpus to the constructor `DocumentTermMatrix` in order to create a document-term-matrix or a term-document matrix. Objects derived from either share the same methods, but differ in how the underlying matrix is represented: A `DocumentTermMatrix` has documents on its rows and columns corresponding to words, whereas a `TermDocumentMatrix` has rows corresponding to words and columns to documents.\n\n``` javascript\nvar terms = new tm.DocumentTermMatrix( my_corpus );\n```\n\nAn instance of either `DocumentTermMatrix` or `TermDocumentMatrix` has the following properties:\n\n### Properties\n\n#### `.vocabulary`\nAn array holding all the words occuring in the corpus, in order corresponding to the column entries of the document-term matrix.\n\n#### `.data`\nThe document-term or term-document matrix, implemented as a nested array in JavaScript. Rows correspond to individual documents, while each column index corresponds to the respective word in `vocabulary`. Each entry of `data` holds the number of counts the word appears in the respective documents. The array is sparse, such that each entry which is undefined corresponds to a value of zero.\n\n#### `.nDocs`\nThe number of documents in the term matrix\n\n#### `.nTerms`\nThe number of distinct words appearing in the documents\n\n### Methods\n\n#### `.findFreqTerms( n )`\n\nReturns all terms in alphabetical ordering which appear `n` or more times in the corpus. The return value is an array of objects of the form\n`{word: \"<word>\", count: <number>}`.\n\n#### `.removeSparseTerms( percent )`\n\nRemove all words from the document-term matrix which appear in less than `percent` of the documents.\n\n#### `.weighting( fun )`\n\nApply a weighting scheme to the entries of the document-term matrix. The `weighting` method expects a function as its argument, which is then applied to each entry of the document-term matrix. Currently, the function `weightTfIdf`, which calculates the term-frequency inverse-document-frequency (TfIdf) for each word, is the only built-in weighting function.  \n\n#### `.fill_zeros()`\n\nTurn the document-term matrix `dtm` into a non-sparse matrix by replacing each value which is `undefined` by zero and save the result.\n\n## Utils\n\nThe module exports several other utility functions.\n\n#### `.expandContractions( str )`\n\nReplaces all occuring English contractions by their expanded equivalents, e.g. \"don't\" is changed to\n\"do not\". The resulting string is returned.\n\n#### `.weightTfIdf( terms )`\n\nWeights document-term or term-document matrix `terms` by term frequency - inverse document frequency. *Mutates* the input `DocumentTermMatrix` or `TermDocumentMatrix` object.\n\n## Data\n\n#### .STOPWORDS\n\nAn object with four keys: `DE`, `EN`, `ES` and `IT`, each of which is an `array` of stopwords for the German, English, Spanish and Italian language, respectively.\n\n``` javascript\n{\n\t\"EN\": [\n\t\t\"a\",\n\t\t\"a's\",\n\t\t\"able\",\n\t\t\"about\",\n\t\t\"above\",\n\t\t// (...)  \n\t],\n\t\"DE\": [\n\t\t// (...)\n\t],\n\t// (...)\n}\n```\n\n#### .CONTRACTIONS\n\nThe keys of the `CONTRACTIONS` object are the contracted expressions and the corresponding values are `arrays` of the possible expansions.\n\n``` javascript\n{\n\t\"ain't\": [\"am not\", \"are not\", \"is not\", \"has not\",\"have not\"],\n\t\"aren't\": [\"are no\", \"am not\"],\n\t\"can't\": [\"cannot\"],\n\t// (...)\n}\n```\n\n## Unit Tests\n\nRun tests via the command `npm test`\n\n---\n## License\n\n[MIT license](http://opensource.org/licenses/MIT).\n\n[npm-image]: https://badge.fury.io/js/text-miner.svg\n[npm-url]: http://badge.fury.io/js/text-miner\n\n[travis-image]: https://travis-ci.org/Planeshifter/text-miner.svg\n[travis-url]: https://travis-ci.org/Planeshifter/text-miner\n\n[codecov-image]: https://img.shields.io/codecov/c/github/Planeshifter/text-miner/master.svg\n[codecov-url]: https://codecov.io/github/Planeshifter/text-miner?branch=master\n","maintainers":[{"name":"planeshifter","email":"pgb@andrew.cmu.edu"}],"time":{"modified":"2023-01-21T21:42:51.139Z","created":"2014-08-15T11:55:57.211Z","0.0.0":"2014-08-15T11:55:58.590Z","0.1.0":"2014-09-12T04:00:47.810Z","0.1.2":"2014-09-12T04:09:21.510Z","0.1.3":"2014-09-12T04:10:18.601Z","0.1.4":"2014-10-13T03:56:34.645Z","0.1.5":"2014-11-25T04:31:53.822Z","0.1.6":"2014-11-30T14:48:02.144Z","0.1.7":"2014-12-01T16:15:38.760Z","0.1.8":"2014-12-01T16:18:03.169Z","0.1.9":"2015-01-17T22:46:19.112Z","0.1.10":"2015-01-17T22:50:39.085Z","0.1.11":"2015-01-17T22:51:56.426Z","0.1.12":"2015-01-20T04:07:03.558Z","0.1.13":"2015-01-29T20:18:16.559Z","0.1.14":"2015-01-29T20:22:34.830Z","0.1.15":"2015-03-17T04:48:36.157Z","0.1.16":"2015-03-17T04:52:14.833Z","0.1.17":"2015-03-17T05:01:34.273Z","0.2.0":"2015-03-23T05:08:43.995Z","1.0.0":"2015-04-09T21:13:35.058Z","1.0.1":"2015-05-21T21:43:37.022Z","1.0.2":"2015-05-21T21:57:34.742Z","1.0.3":"2015-05-22T00:31:11.076Z","1.0.4":"2015-09-19T03:51:45.889Z","1.0.5":"2015-09-19T03:53:42.676Z","1.0.6":"2018-05-06T05:14:48.755Z","1.2.0":"2019-06-13T07:41:53.673Z","1.3.0":"2019-07-23T03:10:22.414Z","2.0.0":"2023-01-21T21:42:51.058Z"},"readmeFilename":"README.md","keywords":["text-mining","text-analytics","document-term-matrix","dtm","text-analysis"],"repository":{"type":"git","url":"git+https://github.com/Planeshifter/text-miner.git"},"author":{"name":"Philipp Burckhardt","email":"pburckhardt@outlook.com"},"bugs":{"url":"https://github.com/Planeshifter/text-miner/issues"},"license":"MIT","homepage":"https://github.com/Planeshifter/text-miner#readme","users":{"nguru":true,"paul-byford":true,"nimblemachine":true,"crishernandezco":true,"hirmes":true,"patoi":true,"wozacosta":true,"leviz":true,"delacap":true,"rkopylkov":true}}