报告详情
Enhancing Contextual Understanding in Sentiment Analysis Using Advanced Transformer Architectures.
编号：144
访问权限：仅限参会人
更新：2026-07-25 16:44:07 浏览：18次
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摘要
Transformer-based models have improved sentiment analysis greatly, but it is difficult to detect contextual complexities, including sarcasm, irony, and implicit sentiment. This work aims to solve this problem by assessing state-of-the-art transformer designs and suggesting an ensemble architecture to provide a better understanding of the context. They are initially evaluated on baseline models like BERT and RoBERTa and then advanced models like T5 and ELECTRA are used in order to enhance context-aware representation. Various benchmark datasets were experimented with, such as IMDB, Twitter, and a sarcasm dataset, to take into account a variety of linguistic properties. Accuracy and F1-score were used as measures of performance, and the context-sensitive sentiment detection was specifically considered. The findings reveal that although more sophisticated models are better than the basic models, the proposed ensemble framework is always superior in all datasets. In particular, it shows better ability to recognize subtle sentiment expression, particularly in contexts with sarcasm. In general, the work indicates the importance of combining various transformer models to improve the contextual knowledge and gives a viable way forward in designing an effective sentiment analysis system.
关键词
Sentiment Analysis, Transformer Models, Contextual Understanding, Ensemble Learning, Sarcasm Detection
报告人
V Mallesi
Research Scholar Research Scholar； India； Andhra Pradesh； JNTUA Department of Computer Science and Engineering G Pulla Reddy Engineering College(Autonomous)； Kurnool； 518007稿件作者
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重要日期
-
会议日期
07月30日
2026
至08月01日
2026
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07月28日 2026
初稿截稿日期
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08月03日 2026
注册截止日期
主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICNCCom 2026
- ic******@usssociety.org
历届会议
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2026年07月30日 印度 Trichy
2026 International Conference on Networks Computers and Communications
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