Summary of the paper
| Title | Evaluating Humour Features on Web Comments |
|---|---|
| Authors | Antonio Reyes, Martin Potthast, Paolo Rosso and Benno Stein |
| Abstract | Research on automatic humor recognition has developed several features whichdiscriminate funny text from ordinary text. The features have been demonstratedto work well when classifying the funniness of single sentences up to entireblogs. In this paper we focus on evaluating a set of the best humor featuresreported in the literature over a corpus retrieved from the Slashdot Web site.The corpus is categorized in a community-driven process according to thefollowing tags: funny, informative, insightful, offtopic, flamebait,interesting and troll. These kinds of comments can be found on almost everylarge Web site; therefore, they impose a new challenge to humor retrieval sincethey come along with unique characteristics compared to other text types. Iffunny comments were retrieved accurately, they would be of a greatentertainment value for the visitors of a given Web page. Our objective, thus,is to distinguish between an implicit funny comment from a not funny one. Ourexperiments are preliminary but nonetheless large-scale: 600,000 Web comments.We evaluate the classification accuracy of naive Bayes classifiers, decisiontrees, and support vector machines. The results suggested interesting findings. |
| Language | Other |
| Topics | Document Classification, Text categorisation, Emotion Recognition/Generation, Other |
Full paper  |
Evaluating Humour Features on Web Comments |
| Bibtex | @InProceedings{REYES10.731, author = {Antonio Reyes, Martin Potthast, Paolo Rosso and Benno Stein}, title = {Evaluating Humour Features on Web Comments}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |