Summary of the paper
| Title | Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing |
|---|---|
| Authors | Elena Filatova |
| Abstract | The ability to reliably identify sarcasm and irony in text can improve the performance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sarcasm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impossible to identify a sentence containing sarcasm without knowing the context. In this paper we describe a corpus generation experiment where we collect regular and sarcastic Amazon product reviews. We perform qualitative and quantitative analysis of the corpus. The resulting corpus can be used for identifying sarcasm on two levels: a document and a text utterance (where a text utterance can be as short as a sentence and as long as a whole document). |
| Topics | Corpus (creation, annotation, etc.), Document Classification, Text categorisation, Emotion Recognition/Generation |
Full paper  |
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing |
| Bibtex | @InProceedings{FILATOVA12.661, author = {Elena Filatova}, title = {Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |