####Getting Started Install package with:

  npm install document-tfidf

####Features:

• countTermFrequencies
• storeTermFrequencies
• normalizeTermFrequencies
• identifyUniqueTerms
• fullTFIDFAnalysis

Documentation

• Term Frequency - Inverse Document Frequency (TFIDF) Module:
  • countTermFrequencies: function(text [, options])
    • Counts the number of times each token appears in the input text.
    • Current options include tokenLength, which dictates the number of words that comprise each token. tokenLength defaults to 1.
    • Depends on nGrams module, which can get all tokens with arbitrary length.
  • storeTermFrequencies: function(tokenSet, TFStorage)
    • Adds the tokenSet to the collectionStorage for improved analysis over time.
    • It’s recommended to save this collection in a persistent data store, although this is unnecessary.
    • If collectionStorage is not provided, it will create it as an object and return that object.
  • normalizeTermFrequencies: function(tokenSet, TFStorage)
    • For each token in tokenSet, normalizeTermFrequencies will divide its count by the total number found in TFStorage and return the token set with normalized counts.
  • identifyUniqueTerms: function(normalizedTokenSet [, options])
    • From the input normalizedTokenSet, identifyUniqueTerms will return the most unique tokens, as defined by the highest TFIDF
    • Current options include uniqueThreshold. If specified, identifyUniqueTerms will return all terms with a TFIDF equal to or greater than the uniqueThreshold
  • fullTFIDAnalysis: function(text [, options])
    • Completes all of the above TFIDF calculations
    • options correspond with the options for each piece of the analysis

View the full specs and check out more text analysis in my Text Analysis Suite.