#HashtagWars: Learning a Sense of Humor
This work addresses the need for more nuanced humor analysis in natural language processing, though it is incremental as it builds on existing humor detection methods with a new dataset.
The authors tackled the problem of computational humor by creating a new dataset for comparative humor ranking using tweets as humorous responses to hashtags, achieving 63.7% accuracy with a supervised system, which indicates the task is more challenging than typical humor detection.
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.