Understanding TF-IDF in NLP.

TF-IDF, short for Term Frequency–Inverse Document Frequency, is a numerical statistic that is intended to reflect how important a word is to a document, in a collection or Corpus(Paragraph).It is often used as a Weighing Factor in searches of information retrieval, Text Mining, and User Modelling. The TF-IDF value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. TF-IDF is much more preferred than Bag-Of-Words, in which every word, is represented as 1 or 0, every time it gets appeared in each Sentence, while, in TF-IDF, gives weightage to each Word separately, which in turn defines the importance of each word than others. Let's Consider these Three sentences: Let's assume a word "Good", in sentence 1, as we know, TF(t) (Number of times term t appears in a document) / (Total number of terms in the document). So, Number of times the word "Good" appears in Sentence 1 is, 1 Time, and the Total number of times the word "Good", appears in all three Sentences is 3 times, so the TF(Term Frequency) value of word "Good" is, TF("Good") 1/3 0.333.