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.