Enhanced Sentiment Analysis of Social Media Text Using Transformer-Based Sarcasm Detection

DOI

10.2991/978-94-6239-789-7_22How to use a DOI?

Keywords

Sentiment analysis; BERT; NLP; Sarcasm detection; Recommendation systems

Abstract

NLP has been one of the best frameworks used in text era especially in today’s world of social media and influence. Sentiment analysis though old is still one of the most used tools; however, with increase of sarcasm in social media, traditional systems are failing to accurately understand and interpret. The research proposes a Transformer based model helping in understanding sarcasm which is crucial in sentiment analysis. To understand their clients and comments, many institutions use sentiment analysis. However, the increase in sarcasm has adversely affected sentiment analysis’ efficiency and legitimacy. The proposed system will help elevate the validity and legitimacy of sentiment analysis by integrating sarcasm detection, adding trust to the results.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sudhir Kumar
AU  - Bharti Mittal
AU  - Sangeeta Rani
AU  - Isha Sharma
PY  - 2026
DA  - 2026/10/08
TI  - Enhanced Sentiment Analysis of Social Media Text Using Transformer-Based Sarcasm Detection
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 303
EP  - 314
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_22
DO  - 10.2991/978-94-6239-789-7_22
ID  - Kumar2026
ER  -