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| Content Provider | ACM Digital Library |
|---|---|
| Author | Liu, Naishi Zhang, Renxian |
| 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. |
| Starting Page | 889 |
| Ending Page | 898 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450325981 |
| DOI | 10.1145/2661829.2661997 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Computational humor Machine learning Twitter Humor recognition |
| Content Type | Text |
| Resource Type | Article |
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