BERT Model with Fuzzy Logic Optimization on Multivariate Sentiment Analysis Tasks
Year: 2024
Type: article
Abstract: With the development of natural language processing techniques, sentiment analysis techniques are constantly updated. However, most of the sentiment analysis techniques are limited to handling binary sentiment analysis and perform poorly on multivariate sentiment analysis tasks. This paper explores the optimization in the field of sentiment analysis by using the BERT model combined with fuzzy logic to improve the performance of the model on multivariate sentiment classification tasks. The study uses the Twitter comments dataset and aims to gain insights into the challenges and optimization strategies of sentiment analysis models when dealing with multivariate sentiment. First, a single BERT model is experimentally demonstrated to perform better in binary classification tasks compared to multivariate classification tasks, highlighting its adaptability for simple sentiment classification and providing a basis for comparative experiments for multivariate classification experiments. To further improve the performance in multivariate sentiment analysis, fuzzy logic is introduced in this paper, and through the integration of this fuzzy logic, the accuracy of the BERT model on the multivariate sentiment classification task is successfully improved. The results of the study not only reveal the superiority of a single BERT model in binary classification but also emphasize the importance of incorporating fuzzy logic when facing multivariate sentiment analysis.
DOI: 10.62051/48xrya81
Author: Chengxiang Sun
Institution: Beijing Normal-Hong Kong Baptist University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401552939