Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Fine-Tuned Transformers for Contextual Sentiment Detection in Imbalanced Customer Feedback

Authors
Kirti Wanjale1, *, Sonal Shamkuwar2, Kishor Pathak3, Rohit Wakade4, Tejas Ahire5, Aditya Labhade6
1Professor, Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
2Assistant Professor, Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
3Assistant Professor, Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, India
4Student, Department of of CSE-Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, India
5Student, Department of Electronics and Telecommunications, Vishwakarma Institute of Technology, Pune, Maharashtra, India
6Student, Department of Electronics and Telecommunications, Vishwakarma Institute of Technology, Pune, Maharashtra, India
*Corresponding author. Email: kirti.wanjale@vit.edu
Corresponding Author
Kirti Wanjale
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_17How to use a DOI?
Abstract

This work proposes a sentiment analysis method for processing and analyzing customer feedback data of an application developed for online classes and video conferencing. We overcome the problem of an unbalanced dataset by using negation generation methods and oversampling to achieve class balance. A fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model is applied to sentiment classification, obtaining a test set accuracy of 84% with good precision (95%) in identifying negative sentiment and good recall (97%) for positive sentiment classification. The work shows that transformer-based models are effective in handling customer feedback context and sentiment analysis, which has implications for application developers to design better user experience. The methodology developed here can be used in other customer feedback analysis situations of this type, supporting both natural language processing research and applied business use.

Copyright
© 2025 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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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_17How to use a DOI?
Copyright
© 2025 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.

Cite this article

risenwbib
TY  - CONF
AU  - Kirti Wanjale
AU  - Sonal Shamkuwar
AU  - Kishor Pathak
AU  - Rohit Wakade
AU  - Tejas Ahire
AU  - Aditya Labhade
PY  - 2026
DA  - 2026/01/06
TI  - Fine-Tuned Transformers for Contextual Sentiment Detection in Imbalanced Customer Feedback
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 244
EP  - 254
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_17
DO  - 10.2991/978-94-6463-948-3_17
ID  - Wanjale2026
ER  -