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Journal article
Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency
Journal of computer and communications, Vol.10(8), pp.117-128
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.
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- Title
- Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency
- Publication Details
- Journal of computer and communications, Vol.10(8), pp.117-128
- Resource Type
- Journal article
- Publisher
- Scientific Research Publishing, Inc.
- Copyright
- © 2022 by author(s) and Scientific Research Publishing Inc.
- Identifiers
- 99380178992406600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering
- Language
- English