A Research Travelogue on Text Classification

DOI

10.2991/978-94-6463-858-5_46How to use a DOI?

Keywords

Text classification; NLP; Sentiment analysis

Abstract

Text classification is a cornerstone natural language processing (NLP) task that involves giving predetermined labels to a given text. Its applications span several domains, including topic modeling, fake news detection and sentiment analysis. Text categorizing is extended and highlights domain specific applications and ensemble models in text classification research. Modern deep learning strategies, modern hybrid models, and an overview of text classification methods are provided and covered classical techniques. We discuss key challenges such as handling imbalanced datasets, domain adaptation, and interpretability. Additionally, we explore emerging trends, including multimodal classification, and the integration of large language models. We focus on giving discernments into the development of text classification, emphasize its current state, and describe future directions in research.

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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TY  - CONF
AU  - B. Anitha
AU  - B. Rama
PY  - 2025
DA  - 2025/11/04
TI  - A Research Travelogue on Text Classification
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 532
EP  - 541
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_46
DO  - 10.2991/978-94-6463-858-5_46
ID  - Anitha2025
ER  -