An Empirical Comparative Study of Machine Learning Algorithms for Telugu News Classification
- DOI
- 10.2991/978-94-6463-314-6_12How to use a DOI?
- Keywords
- Random Kitchen Sink (RKS); Logistic regression; Multinomial Naive Bayes; Multilayer Perceptron; Natural Language Processing (NLP); Deep Learning; Classification
- Abstract
Amidst escalating data growth, effective classification in diverse domains, including the news industry, is imperative. However, relying solely on human intervention for classification is unfeasible. Addressing the complexities of the Telugu language and leveraging Natural Language Processing (NLP), this study employs classification techniques. Custom Machine Learning and Deep Learning models are developed, utilizing various word embeddings, aiming to enhance accuracy and efficiency in categorizing newspaper articles. The research tackles challenges of unstructured text, attributes, NLP techniques, missing metadata, and algorithm selection. The proposed model offers both generality and efficiency, systematically classifying text documents and demonstrating significant improvements in accuracy through innovative techniques.
- Copyright
- © 2023 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
TY - CONF AU - S. V. S. Dhanush AU - Tsaliki Satya Ganesh Kumar AU - Dharavathu Rohith AU - Penaka Vishnu Reddy AU - K. P. Soman AU - S. Sachin Kumar PY - 2023 DA - 2023/12/21 TI - An Empirical Comparative Study of Machine Learning Algorithms for Telugu News Classification BT - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023) PB - Atlantis Press SP - 117 EP - 127 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-314-6_12 DO - 10.2991/978-94-6463-314-6_12 ID - Dhanush2023 ER -