Article citationsMore>>

Tiwari, S., Verma, A., Garg, P. and Bansal, D. (2020) Social Media Sentiment Analysis on Twitter Datasets. 2020 6th International Conference on Advanced Computing & Communication Systems (ICACCS), 6-7 March 2020, Coimbatore.
https://doi.org/10.1109/ICACCS48705.2020.9074208

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.