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has been cited by the following article:

• TITLE: Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency

  AUTHORS: Akash Addiga, Sikha Bagui

  KEYWORDS: Sentiment Analysis, Twitter Data, Term Frequency, Inverse Term Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), Social Media

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.8, August 30, 2022

  ABSTRACT: This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness of this work is established by determining the overall sentiment of a politician’s tweets based on TF-IDF values of terms used in their published tweets. By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity. The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.