MixSarc: A Bangla-English Code-Mixed Corpus For Implicit Meaning Identification

Abstract

Thisthesisfocusesondetectinghumor,sarcasm,offensiveness,andvulgarityinBangla English code-mixed text, an area largely overlooked in existing natural language pro cessing (NLP) research. A novel dataset has been proposed, which will be created by scraping and filtering social media content, followed by manual annotation across fourattributes. Twotransformer-basedapproacheswereexploredinsmallscale: multi class and multi-label text classification. The study also proposes future directions, in cluding dataset balancing, comparative evaluation of transformer models and large language models (LLMs), and the introduction of a SarOff Score to better capture sarcasm-offense overlap. By addressing the complexities of code-mixed tone detec tion, this work advances NLP in low-resource, multilingual settings