Automatic Detection of Irony

Conclusion

Our aims in this study were twofold: (1) to propose the first system for irony detection in social media content in French, and (2) to assess the portability of this system for other languages. The field of language processing to which our work is intended to contribute is a particularly active one, notably due to the importance of irony and sarcasm detection for improving the performance of opinion analysis systems.

Our first task was to establish a full state of the art concerning linguistic and computational approaches for the detection of figurative language. While our work focused specifically on irony and sarcasm, we also described other authors’ contributions in areas such as humor, satire, metaphor and comparison, as the borders between these phenomena are somewhat permeable. Based on our literature review, we made two main observations:

1. 1) Research in the field of linguistics has approached figurative language from a semantic and pragmatic perspective, concentrating on the mechanisms involved in linguistic expressions of this type of language. These include hyperbole, rhetorical questions, false assertions, etc. Work in this area tends to focus on literary works, such as novels or poetry.
2. 2) In computational work, irony has mostly been considered as a generic term, extended to cover sarcasm and, in some cases, satire. Studies in this area have made extensive use of social networks, such as Twitter; the presence of specific hashtags indicating the use of ...