Abstract
We describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, and text-based sentiment features for opinion classification. We achieve good results in experiments using supervised learning machine over syntactic and sentiment-based features. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents.- Anthology ID:
- L10-1243
- Volume:
- Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
- Month:
- May
- Year:
- 2010
- Address:
- Valletta, Malta
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/354_Paper.pdf
- DOI:
- Cite (ACL):
- Horacio Saggion and Adam Funk. 2010. Interpreting SentiWordNet for Opinion Classification. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
- Cite (Informal):
- Interpreting SentiWordNet for Opinion Classification (Saggion & Funk, LREC 2010)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/354_Paper.pdf