Researchers eye-view of sarcasm detection in social media textual content
This is an incremental review paper addressing the problem of sarcasm detection for researchers and developers in natural language processing, with no new methods or data introduced.
The paper reviews the challenge of detecting sarcasm in social media text, noting the lack of exact rules for accurate detection, and concludes by discussing various techniques, datasets, and researcher challenges without presenting new results or concrete numbers.
The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challenging task for machines to differentiate sarcastic sentences from non-sarcastic sentences. There are no exact rules based on which model will accurately detect sarcasm from many text corpus in the current situation. So, one needs to focus on optimistic and forthcoming approaches in the sarcasm detection domain. This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features, and the researcher's challenges.