Get The Classic TF-IDF Vector Space Model

Bag of Words Thus the vector for a document has the frequency of each word in the corpus for that document. The key difference between bag of words and TF-IDF is that the former does not incorporate any sort of inverse document frequency (IDF) and is only a frequency count (TF).

Types of Information Retrieval (IR) Model Boolean, Vector and Probabilistic are the three classical IR models.

What is a Vector Space Model? Vector space models are algebraic models that are often used to represent text (although they can represent any object) as a vector of identifiers. With these models, we are able to identify whether various texts are similar in meaning, regardless of whether they share the same words.

A vector space model is an algebraic model, involving two steps, in first step we represent the text documents into vector of words and in second step we transform to numerical format so that we can apply any text mining techniques such as information retrieval, information extraction,information filtering etc.

The Vector Space Model was developed in the 1960s and 1970s by Salton; to test his model, Salton experimented with the SMART system (System for the Mechanical Analysis and Retrieval of Text) (Salton, 1971; Salton & Lesk, 1965).

3.2 Inverse document frequency (IDF) Inverse Document Frequency (IDF) is a popular measure of a word's importance. It's defined as the logarithm of the ratio of number of documents in a collection. to the number of documents containing the given word[16]. This means rare.

Vector space models are to consider the relationship between data that are represented by vectors. It is popular in information retrieval systems but also useful for other purposes. Generally, this allows us to compare the similarity of two vectors from a geometric perspective.

Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers (such as index terms). It is used in information filtering, information retrieval, indexing and relevancy rankings.