Title: Multilingual language classification model for offensive comments categorisation in social media using HAMMC tree search with enhanced optimisation technique

Authors: B. Aarthi; Balika J. Chelliah

Addresses: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India

Abstract: The exponential rise of social media platforms has led to a surge in offensive content, highlighting the necessity for effectively detecting and managing such comments. This necessitates precise and advanced online social networks (OSN) categorisation and optimisation methods. This study introduces and assesses a novel technique for automatically categorising texts, supporting over 60 languages, without relying on a pre-annotated dataset. The technique employs multilingual methods based on the randomised explicit semantic analysis (ESA) strategy. To combat the inherently multilingual nature of social media content, the paper introduces an innovative classification and optimisation strategy named 'hybrid adaptive Markov chain Monte Carlo tree search (HAMCMTS) with enhanced eagle Aquila optimiser (EEAO)'. The study uses three publicly available datasets to identify negative or offensive comments in various languages, offering a comprehensive analysis in this field. The proposed approach holds potential for diverse applications, particularly in multilingual categorisation tasks like monitoring disaster-related communications on social media to improve visibility and trust. Moreover, it incorporates a sophisticated mechanism to bolster the dependability of its recommendations.

Keywords: negative or offensive comments; multilingual languages; explicit semantic analysis; ESA; enhanced eagle Aquila optimiser; EEAO; hybrid adaptive Markov chain Monte Carlo tree search; online social networks; OSN.

DOI: 10.1504/IJCSE.2025.148731

International Journal of Computational Science and Engineering, 2025 Vol.28 No.5, pp.498 - 514

Received: 13 Sep 2023
Accepted: 02 Jul 2024
Published online: 22 Sep 2025 *

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