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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Natural Language Processing
Volume 7 - 2024 | doi: 10.3389/frai.2024.1481870

A new optimized hybrid machine learning approach for enhancing sentiment analysis on social media

Provisionally accepted
  • Florida Institute of Technology, Melbourne, United States

The final, formatted version of the article will be published soon.

    Understanding public sentiment on social media platforms like Twitter and Facebook has become increasingly crucial in various domains, including marketing, politics, and customer service. Social media serves as a rich source of real-time data, providing insights into public opinion towards products, services, events, and social issues. This study focuses on enhancing sentiment analysis on different social datasets using advanced machine learning techniques. In this research, we present a new approach for sentiment analysis, leveraging a hybrid model combining Particle Swarm Optimization with BiLSTM neural networks. The proposed hybrid PSO-BiLSTM model aims to optimize key parameters of the BiLSTM architecture, including the number of hidden layers, neurons, and training epochs, to enhance the predictive accuracy of sentiment analysis tasks.We conducted experiments on three benchmark datasets and compared the performance of the proposed model with standard BiLSTM and other existing approaches. The results demonstrate that the hybrid model consistently outperforms baseline models, achieving higher accuracy rates in predicting text sentiment. Through the adaptive tuning facilitated by PSO algorithm, the proposed model demonstrates its effectiveness in optimizing BiLSTM parameters, leading to improved performance across diverse datasets. This research contributes to advancing sentiment analysis techniques, providing a robust framework for automatic opinion discernment with practical implications for various domains.

    Keywords: BiLSTM, hyperparameter tuning, Natural Language Processing, Particle Swarm Optimization, sentiment analysis

    Received: 16 Aug 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Helforoush and Hlaing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zarindokht Helforoush, Florida Institute of Technology, Melbourne, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.