Presentation Details
Enhancing Contextual Understanding in Sentiment Analysis Using Advanced Transformer Architectures.
ID：144
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Updated Time：2026-07-25 16:44:07 Hits：14
Online
Start Time：2026-07-31 14:40（Asia/Kolkata）
Duration：15min
Session：S7 Disruptive Technologies for Manufacturing »
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Abstract
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.
Keywords
Sentiment Analysis, Transformer Models, Contextual Understanding, Ensemble Learning, Sarcasm Detection
Speaker
V Mallesi
Research Scholar Research Scholar； India； Andhra Pradesh； JNTUA Department of Computer Science and Engineering G Pulla Reddy Engineering College(Autonomous)； Kurnool； 518007Submission Author
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Important Date
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Conference Date
Jul 30
2026
toAug 01
2026
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Jul 28 2026
Registration deadline
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Jul 30 2026
Draft paper submission deadline
Sponsored By
The United Societies of Science
Organized By
Kongunadu College of Engineering and Technology
Supported By
IEEE Section
IEEE Madras Section
IEEE Madras Section
Contact Information
Previous Conferences
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2026-07-30 India Trichy
2026 International Conference on Networks Computers and Communications
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