An exploratory study of conventional machine learning and large language models for sentiment analysis

• Main Authors: ZOU, Cui, CAI, Jingyuan, CHEN, Langtao, NAH, Fiona Fui-hoon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Sentiment Analysis; Large Language Models; GPT; FLAN-T5; Machine Learning; IMDb; Movie Reviews; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9961; https://ink.library.smu.edu.sg/context/sis_research/article/10961/viewcontent/ExploratoryStudy_ML_LLM_av.pdf
• Institution: Singapore Management University

Sentiment analysis is the use of natural language processing to identify affective states and determine people’s opinions in various analytical applications such as customer reviews and social media analyses. Large language models (LLMs) such as GPT-4o demonstrate impressive performance in text generation tasks. Despite numerous studies in the extant literature, few have compared the performance of conventional machine learning models with LLMs for sentiment analysis. This study aims to fill this gap by conducting an evaluation of these models using a balanced dataset of 2,000 IMDb movie reviews. Our study shows that GPT-4o achieves the highest performance, while GPT-3.5 and FLAN-T5 models also show strong performance, being slightly below that of GPT-4o. Advanced LLMs outperform conventional machine learning models. Our findings highlight the advanced capabilities and user-friendliness of LLMs compared to conventional machine learning models. This research underscores the rapid evolution of LLMs for sentiment analysis.