ENHANCED HATE SPEECH CLASSIFICATION USING PHRASE REPLACEMENT AND FUZZY-BASED MODIFIED BERT MODEL

ENHANCED HATE SPEECH CLASSIFICATION USING PHRASE REPLACEMENT AND FUZZY-BASED MODIFIED BERT MODEL | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 October 2025 V1 Latest version Share on ENHANCED HATE SPEECH CLASSIFICATION USING PHRASE REPLACEMENT AND FUZZY-BASED MODIFIED BERT MODEL Authors : Ms.Rupashini P R [email protected] and Dr. K.Premalatha Authors Info & Affiliations https://doi.org/10.22541/au.175975574.49248988/v1 124 views 84 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The identification and classification of hate speech is very importance during the current digital world since this might aid in reducing negative activities on the internet and creating a safe online space. Several methodologies have been in use for this purpose, such as from the traditional approach to machine learning methods like SVM and logistic regression, to the recent ones with LSTM and BERT. These models, however, are facing serious drawbacks: biased training data, context dependency, and scalability issues. In addressing these challenges, a new approach is proposed. On the side of data pre-processing, SBPR enhances the input text by replacing sensitive phrases with sentiment-neutral synonyms and reduces biased training data. STea revolutionizes feature extraction to handle semantic context better and provides better understanding to the model. The workings of the proposed solution revolve around a BERT model amended with a Fuzzy Logic-based Attention Mechanism for classification. This novel approach exploits FLAM’s potential for capturing subtle linguistic cues and putting more attention to relevant information, hence improving accuracy and interpretability in classification tasks. Results on this proposed methodology across a wide variety of five datasets, like Twitter, Reddit, Facebook, Sarcasm, and Jigsaw Toxic, prove fairly encouraging. Whereas the model showed good performance without overfitting or getting stuck in a local minimum with such an impressive accuracy rate of 97% and minimal loss of 0.1, it establishes stability and dependability. Supplementary Material File (bert copy.docx) Download 1.53 MB Information & Authors Information Version history V1 Version 1 06 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bert model fuzzy logic-based attention mechanism (flam) hate speech detection semantic topic extraction and analysis (stea) sentiment-based phrase replacement (sbpr) Authors Affiliations Ms.Rupashini P R [email protected] Kumaraguru College of Technology View all articles by this author Dr. K.Premalatha Bannari Amman Institute of Technology Department of Computer Science and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 124 views 84 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ms.Rupashini P R, Dr. K.Premalatha. ENHANCED HATE SPEECH CLASSIFICATION USING PHRASE REPLACEMENT AND FUZZY-BASED MODIFIED BERT MODEL. Authorea . 06 October 2025. DOI: https://doi.org/10.22541/au.175975574.49248988/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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