Term Frequency Inverse Document Frequency

Abstract

Recent advances in computers and technology have resulted in an ever increasing set of documents that will be difficult and time-consuming for the user to retrieve useful information from them. The need is to find a set of data of high relevance from the given document. In this paper, we will apply Term Frequency Inverse Document Frequency (TF-IDF) to determine what set of words in a collection of written text is of high relevance. As the term indicates, TF-IDF will calculate a value for each word in a document. Word with high TF-IDF numbers will imply a strong relationship with the document they appear in, suggesting that, if that is included in the document, the document can be of great interest to the user and vice-versa.

Keywords: Artificial intelligence, k-mean clustering, machine learning, relevance of words to documents, TF-IDF

Cite this Article: Utkarsh Sharma, Anay Jain, Anshita. Term Frequency Inverse Document Frequency. Journal of Multimedia Technology & Recent Advancements. 2020; 7(1): 1–6p.