Abstract
The impact of offensive language on public and professional discourse highlights the need for efficient mitigating measures. Cutting-edge computational linguistic techniques were used to identify and treat such language in a novel way. A two-pronged mechanism is used when hazardous content is found: offending terminology is either removed or put through Natural Language Pre-processing, producing rephrased information that maintains the original meaning of the text. Additionally, this work uses two freely accessible datasets for text categorization. The technique is unique, because during the rephrasing stage, we consider the incorrect words to get their synonyms, and we choose to fit for replacement in the phrase. Classification best accuracy we have achieved of about 95%. The method is comprehensive and aims to create a setting that encourages courteous and peaceful discussion while maintaining semantic integrity. This research provides a sophisticated approach to fostering meaningful relationships in both public and professional contexts by fully addressing incorrect language.
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Jain, S., Tripathy, B.K. (2024). Inappropriate Text Detection and Rephrasing Using NLP. In: Patel, K.K., Santosh, K., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2023. Communications in Computer and Information Science, vol 2030. Springer, Cham. https://doi.org/10.1007/978-3-031-53731-8_21
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