Open Access

ARTICLE

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

by Mahesh B. Shelke¹, Jeong Gon Lee^2,*, Sovan Samanta³, Sachin N. Deshmukh¹, G. Bhalke Daulappa⁴, Rahul B. Mannade⁵, Arun Kumar Sivaraman⁶

1 Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, 431004, India
2 Division of Applied Mathematics, Wonkwang University, 460, Iksan-daero, Iksan-Si, Jeonbuk, 54538, Korea
3 Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk, West Bengal, 721636, India
4 Department of Electronics and Telecommunication Engineering, AISSMSCOE, Pune, Maharashtra, 411001, India
5 Department of Information Technology, Government College of Engineering, Aurangabad, Maharashtra, 431005, India
6 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: Jeong Gon Lee. Email:

Computer Systems Science and Engineering 2023, 44(3), 2457-2468. https://doi.org/10.32604/csse.2023.027979

Received 30 January 2022; Accepted 23 March 2022; Issue published 01 August 2022

Abstract

In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

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Keywords

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