#HashtagWars: Learning a Sense of Humor

Peter Potash; Alexey Romanov; Anna Rumshisky

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arXiv · 1612.03216

#HashtagWars: Learning a Sense of Humor

Peter Potash·Alexey Romanov·Anna Rumshisky

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Abstract

In this work, we present a new dataset for computational humor, specifically comparative humor ranking, which attempts to eschew the ubiquitous binary approach to humor detection. The dataset consists of tweets that are humorous responses to a given hashtag. We describe the motivation for this new dataset, as well as the collection process, which includes a description of our semi-automated system for data collection. We also present initial experiments for this dataset using both unsupervised and supervised approaches. Our best supervised system achieved 63.7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks. Initial experiments indicate that a character-level model is more suitable for this task than a token-level model, likely due to a large amount of puns that can be captured by a character-level model.

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BibTeX RIS

Peter Potash, Alexey Romanov, Anna Rumshisky. 2016-12-09. #HashtagWars: Learning a Sense of Humor. https://arxiv.org/abs/1612.03216

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