Sarcasm is algorithmically challenging. It is an antithetic form of human expression. You have to take into account the phenomenon of linguistic ellipsis, which means that words, phrases and clauses are understood via world knowledge and pragmatics. As you have probably researched, typical ML implementations produce average results. Before going into the specs of the embeddings, I believe you have to check your dataset. There is a difference between a headlines dataset produced from publishers and other forms of short text like tweets that are user-generated content. You have to think how intented sarcasm, perceived sarcasm, irony, hashtags, emoticons and other written linguistic expressions present in the domain of sentiment analysis, shape the problem. It is very interesting to see how a LLM performs on this task. I hope you make progress.
jayqd3 t1_j0yx85f wrote
Reply to Sarcasm Detection model [R]. by Business-Ad6451
Hello.
Sarcasm is algorithmically challenging. It is an antithetic form of human expression. You have to take into account the phenomenon of linguistic ellipsis, which means that words, phrases and clauses are understood via world knowledge and pragmatics. As you have probably researched, typical ML implementations produce average results. Before going into the specs of the embeddings, I believe you have to check your dataset. There is a difference between a headlines dataset produced from publishers and other forms of short text like tweets that are user-generated content. You have to think how intented sarcasm, perceived sarcasm, irony, hashtags, emoticons and other written linguistic expressions present in the domain of sentiment analysis, shape the problem. It is very interesting to see how a LLM performs on this task. I hope you make progress.