A Robust Framework for Sentiment Classification in Textual Data

I. Sundara Siva Rao; Gosu Naveen; Bevara Navya Sri; Bandaru Devi Laxmi Maruthi Kumar

doi:10.2991/978-94-6239-616-6_27

<Previous Article In Volume

Next Article In Volume>

A Robust Framework for Sentiment Classification in Textual Data

Authors

I. Sundara Siva Rao¹^{, *}, Gosu Naveen¹, Bevara Navya Sri¹, Bandaru Devi Laxmi Maruthi Kumar¹

¹CSE (Data Science), Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

^*Corresponding author. Email: isro75@gmail.com

Corresponding Author

I. Sundara Siva Rao

Available Online 31 March 2026.

DOI: 10.2991/978-94-6239-616-6_27 How to use a DOI?
Keywords: Sentiment Analysis; Natural Language Processing; Distil-BERT; Machine Learning; Text Classification
Abstract: This paper presents a holistic end-to-end framework for sentiment analysis, incorporating sophisticated machine learning methodologies within a scalable web application. The framework ensures that sentiment categorisation is accurate and fast by combining DistilBERT with strong preprocessing and deployment methods. This makes a usable, scalable, and secure. For example, academic research can be turned into useful tools that help people make decisions in real time in areas like corporate intelligence, digital marketing, and social media monitoring. The modular design also makes sure that the system can be changed to add new features, such as the advanced visualisation tools, batch processing, and support for multiple languages. Considering all of this, trans-former-based sentiment analysis systems have gone from being research prototypes to useful tools that last a long time and meet both user needs and technical challenges.
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.

Download article (PDF)

<Previous Article In Volume

Next Article In Volume>

Volume Title: Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 31 March 2026
ISBN: 978-94-6239-616-6
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-616-6_27 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - I. Sundara Siva Rao
AU  - Gosu Naveen
AU  - Bevara Navya Sri
AU  - Bandaru Devi Laxmi Maruthi Kumar
PY  - 2026
DA  - 2026/03/31
TI  - A Robust Framework for Sentiment Classification in Textual Data
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 319
EP  - 338
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_27
DO  - 10.2991/978-94-6239-616-6_27
ID  - Rao2026
ER  -

download .riscopy to clipboard