Future of Information and Communication Conference (FICC) 2025
28-29 April 2025
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: When conducting sentiment analysis on social networks, facing the challenge of temporal and multi-modal data, it is necessary to enable the model to deeply mine and combine information from various modalities. Therefore, this study constructs an emotion analysis model based on multitask learning. This model utilizes a comprehensive framework of convolutional networks, bidirectional gated recurrent units, and multi head self attention mechanisms to represent single modal temporal features in an innovative way, and adopts a cross modal feature fusion strategy. The experiment showed that the model accomplished 0.83 average precision and a 0.83 F1-value, respectively. In contrast with multi-scale attention (0.69, 0.70), aspect-based sentiment analysis (0.78, 0.74), and long short-term memory network (0.71, 0.78) models, this model demonstrated higher robustness and classification accuracy. Especially in terms of parallel computing efficiency, the acceleration ratio of the model reached 1.61, which is the highest among all compared models, highlighting the potential for time savings in large data volumes. This study has shown good performance in sentiment analysis in social networks, providing a novel perspective for solving complex sentiment classification problems.
Lin He and Haili Lu, “The Performance of a Temporal Multi-Modal Sentiment Analysis Model Based on Multitask Learning in Social Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01505112
@article{He2024,
title = {The Performance of a Temporal Multi-Modal Sentiment Analysis Model Based on Multitask Learning in Social Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01505112},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01505112},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Lin He and Haili Lu}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.