@inproceedings{shanmugavadivel-etal-2025-beyond,
title = "{B}eyond{\_}{T}ech@{D}ravidian{L}ang{T}ech 2025: Political Multiclass Sentiment Analysis using Machine Learning and Neural Network",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
R, Sanjai and
Sameer, Mohammed and
K, Motheeswaran",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.23/",
doi = "10.18653/v1/2025.dravidianlangtech-1.23",
pages = "139--143",
ISBN = "979-8-89176-228-2",
abstract = "Research on political feeling is essential for comprehending public opinion in the digital age, as social media and news platforms are often the sites of discussions. To categorize political remarks into sentiments like positive, negative, neutral, opinionated, substantiated, and sarcastic, this study offers a multiclass sentiment analysis approach. We trained models, such as Random Forest and a Feedforward Neural Network, after preprocessing and feature extraction from a large dataset of political texts using Natural Language Processing approaches. The Random Forest model, which was great at identifying more complex attitudes like sar casm and opinionated utterances, had the great est accuracy of 84{\%}, followed closely by the Feedforward Neural Network model, which had 83{\%}. These results highlight how well political discourse can be analyzed by combining deep learning and traditional machine learning techniques. There is also room for improvement by adding external metadata and using sophisticated models like BERT for better sentiment classification."
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%0 Conference Proceedings
%T Beyond_Tech@DravidianLangTech 2025: Political Multiclass Sentiment Analysis using Machine Learning and Neural Network
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A R, Sanjai
%A Sameer, Mohammed
%A K, Motheeswaran
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F shanmugavadivel-etal-2025-beyond
%X Research on political feeling is essential for comprehending public opinion in the digital age, as social media and news platforms are often the sites of discussions. To categorize political remarks into sentiments like positive, negative, neutral, opinionated, substantiated, and sarcastic, this study offers a multiclass sentiment analysis approach. We trained models, such as Random Forest and a Feedforward Neural Network, after preprocessing and feature extraction from a large dataset of political texts using Natural Language Processing approaches. The Random Forest model, which was great at identifying more complex attitudes like sar casm and opinionated utterances, had the great est accuracy of 84%, followed closely by the Feedforward Neural Network model, which had 83%. These results highlight how well political discourse can be analyzed by combining deep learning and traditional machine learning techniques. There is also room for improvement by adding external metadata and using sophisticated models like BERT for better sentiment classification.
%R 10.18653/v1/2025.dravidianlangtech-1.23
%U https://aclanthology.org/2025.dravidianlangtech-1.23/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.23
%P 139-143
Markdown (Informal)
[Beyond_Tech@DravidianLangTech 2025: Political Multiclass Sentiment Analysis using Machine Learning and Neural Network](https://aclanthology.org/2025.dravidianlangtech-1.23/) (Shanmugavadivel et al., DravidianLangTech 2025)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Sanjai R, Mohammed Sameer, and Motheeswaran K. 2025. Beyond_Tech@DravidianLangTech 2025: Political Multiclass Sentiment Analysis using Machine Learning and Neural Network. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 139–143, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.