Summary of the paper
| Title | Modelling Irony in Twitter: Feature Analysis and Evaluation |
|---|---|
| Authors | Francesco Barbieri and Horacio Saggion |
| Abstract | Irony, a creative use of language, has received scarce attention from the computational linguistics research point of view. We propose an automatic system capable of detecting irony with good accuracy in the social network Twitter. Twitter allows users to post short messages (140 characters) which usually do not follow the expected rules of the grammar, users tend to truncate words and use particular punctuation. For these reason automatic detection of Irony in Twitter is not trivial and requires specific linguistic tools. We propose in this paper a new set of experiments to assess the relevance of the features included in our model. Our model does not include words or sequences of words as features, aiming to detect inner characteristic of Irony. |
| Topics | Opinion Mining / Sentiment Analysis, Emotion Recognition/Generation |
Full paper  |
Modelling Irony in Twitter: Feature Analysis and Evaluation |
| Bibtex | @InProceedings{BARBIERI14.231, author = {Francesco Barbieri and Horacio Saggion}, title = {Modelling Irony in Twitter: Feature Analysis and Evaluation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |