• Anshika Mishra
• Anurag Shrivastava
• Ishaan Tripathi
• Riddhisha Srivastava
• Sachin Tripathi

Sarcasm detection is tougher since sometimes it is hard to identify whether the opposite of what is spoken is meant, making it harder for traditional sentiment analysis systems to identify it correctly. In this paper, Tone Tracer, the context-based sarcasm detection using transformer models, is introduced. In this work, both the statement of interest and the context in which it is given are analyzed together. The experiment carried out on various benchmarking datasets shows that Tone Tracer performs better than traditional models with respect to precision, recall, and F1-measure. The significance of using context while detecting sarcasm is clear from these experimental results.

Sarcasm Detection, Context-Aware Learning, Transformer Models, Natural Language Processing, Sentiment Analysis, Conversational Context

"Tone Tracer: Decoding The Hidden Meaning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b46-b52, January-2026, Available :http://www.jetir.org/papers/JETIR2601107.pdf

"Tone Tracer: Decoding The Hidden Meaning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb46-b52, January-2026, Available at : http://www.jetir.org/papers/JETIR2601107.pdf