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Khan, M. and Malviya, A. (2020) Big Data Approach for Sentiment Analysis of Twitter Data Using Hadoop Framework and Deep Learning. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 24-25 February 2020, Vellore.
https://doi.org/10.1109/ic-ETITE47903.2020.201

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.