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Constructing A Corpus Of Figurative Language For a Tweet Classification and Retrieval Task

Twitter is an intriguing source of topical content for tasks involving the detection of phenomena such as sarcasm and metaphor. The hashtags that users employ to self-annotate their own micro-texts can often facilitate the targeted retrieval of texts with the desired characteristics. Though tweets tagged with #sarcasm are highly likely to be sarcastic, the lack of a topic model for sarcastic tweets makes it difficult to detect when such tags are used in the expected way, or indeed, to retrieve tweets that are not explicitly tagged in this way. In this study, we explore how a tweet-retrieval and classification system can benefit from a topic model when constructing a task-specific Twitter corpus, such as for irony, sarcasm or metaphor detection.