Enhancing Sentiment Analysis with GPT—A Comparison of Large Language Models and Traditional Machine Learning Techniques

Tobechi Obinwanne, Patrick Brandtner

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

Sentiment analysis is the process of extracting and analyzing opinions, attitudes, and emotions expressed in text data. Due to the increased amount of user-generated content on the Internet, sentiment analysis has become an important research area, which has successfully been tackled with machine learning models such as SVMs and Random Forests. Against the backdrop of rapidly growing popularity of Generative Pre-trained Transformers (GPT), the question arises, as to how good such models perform in sentiment analysis. This research paper analyzes and compares the performance of GPT-based models with traditional machine learning models for sentiment analysis. The results paint a clear picture: GPTs are a powerful concept that are applicable in sentiment analysis. In our study, they outperform traditional models such as SVM, Random Forests, or Naïve Bayes based on F1-score, precision, accuracy, and AUC-ROC score. As more advanced versions of GPT continue to be developed, it is likely that these models will become even more effective and popular sentiment analysis. Hence, the application and evaluation of GPTs represents a promising avenue for future research.

Original languageEnglish
Title of host publicationIntelligent Sustainable Systems - Selected Papers of WorldS4 2023
EditorsAtulya K. Nagar, Dharm Singh Jat, Durgesh Mishra, Amit Joshi
PublisherSpringer
Pages187-197
Number of pages11
ISBN (Print)9789819975686
DOIs
Publication statusPublished - 2024
EventWorld Conference on Smart Trends in Systems, Security, and Sustainability, WS4 2023 - London, United Kingdom
Duration: 21 Aug 202324 Aug 2023

Publication series

NameLecture Notes in Networks and Systems
Volume803
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceWorld Conference on Smart Trends in Systems, Security, and Sustainability, WS4 2023
Country/TerritoryUnited Kingdom
CityLondon
Period21.08.202324.08.2023

Keywords

  • ChatGPT
  • Generative pre-trained transformers
  • GPT
  • GPT-3.5
  • Large language models
  • Machine learning
  • Sentiment analysis

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