Citation

Kamil, Imran and Packier Mohammad, Nathar Shah (2024) Comparison of Lexicon-Based Method, Machine Learning and Chatgpt on Sentiment Analysis of Big Cap and Small Cap Companies in United State Indexes. Journal of Theoretical and Applied Information Technology, 102 (16). pp. 6280-6287. ISSN 1992-8645

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Abstract

Sentiment analysis is a natural language processing (NLP) method that identifies the sentiment contained in a body of text. It has gained significant attention due to its potential applications in various domains, including finance, marketing, and public opinion monitoring. In the financial sector, sentiment analysis is essential for analyzing market trends, forecasting stock prices, and guiding investment choices. This research paper compares the performance of lexicon-based method, machine learning technique, and ChatGPT in sentiment analysis of big cap and small cap companies in United States indexes using Twitter data. The purpose of implementing ChatGPT is to identify the usefulness of this well-known tool that is currently flooding the social media scene. The results show that Random Forest achieved the highest accuracy overall with 83.6% on big cap and 78.8% on small cap. ChatGPT sentiment has an accuracy of 77.44% on big cap and 72.43% on small cap. Meanwhile the lowest performing method is the TextBlob which has an accuracy of 46.52% on big cap and 43.57% on small cap. Random Forest is able to understand the context of tweets and handle slang terms and phrases, while ChatGPT is still under development but has the potential to perform better in the future. There are many slang terms and phrases that are used in the stock market that are not included in the TextBlob dictionary. Therefore, the performance of TextBlob is the least performing method.

Item Type:	Article
Uncontrolled Keywords:	Sentiment Analysis, Random Forest, Lexicon, Textblob, Machine Learning, Chatgpt
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:16
Last Modified:	01 Oct 2024 01:16
URII:	http://shdl.mmu.edu.my/id/eprint/13054

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