Recognizing Humor on Twitter

Abstract

In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network. The central question of what makes a tweet (Twitter post) humorous drives us to design humor-related features, which are derived from influential humor theories, linguistic norms, and affective dimensions. Using machine learning techniques, we are able to recognize humorous tweets with high accuracy and F-measure. More importantly, we single out features that contribute to distinguishing non-humorous tweets from humorous tweets, and humorous tweets from other short humorous texts (non-tweets). This proves that humorous tweets possess discernible characteristics that are neither found in plain tweets nor in humorous non-tweets. We believe our novel findings will inform and inspire the burgeoning field of computational humor research in the social media.

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