Application of Term Frequency - Inverse Document Frequency in The Naive Bayes Algorithm For ChatGPT User Sentiment Analysis

DOI

10.2991/978-94-6463-413-6_4How to use a DOI?

Keywords

Sentiment Analysis; Naïve Bayes; Term-Frequency

Abstract

Sentiment analysis as part of Natural Language Processing has been widely used to see public sentiment towards a topic. Sentiment analysis functions to classify opinions into positive or negative classifications. In classifying opinions, an algorithm is needed to manage opinion data. One well-known algorithm capable of classifying text data simply and accurately is the naïve Bayes algorithm. Therefore, this research will use the Naive Bayes algorithm which can work well on high-dimensional data. The valid data used in this research is 36,000 ChatGPT user reviews from the Google Play Store, while the outsample data used is 400 tweets from X application users. To increase the classification accuracy value, the naive Bayes algorithm is accompanied by feature weighting using the Term Frequency-Inverse Document Frequency technique. The performance of the classification model shows an accuracy value of 84%, recall of 84%, and precision of 83%. Next, the model classification is stored in pickled form and used to predict outsample data. The predicted data shows data with 208 negative labels and 192 positive labels.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Novita Rajagukguk
AU  - I Putu Eka Nila Kencana
AU  - I G. N. Lanang Wijaya Kusuma
PY  - 2024
DA  - 2024/05/13
TI  - Application of Term Frequency - Inverse Document Frequency in The Naive Bayes Algorithm For ChatGPT User Sentiment Analysis
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 29
EP  - 40
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_4
DO  - 10.2991/978-94-6463-413-6_4
ID  - Rajagukguk2024
ER  -