Summary of the paper
| Title | Interpreting SentiWordNet for Opinion Classification |
|---|---|
| Authors | Horacio Saggion and Adam Funk |
| 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. |
| Topics | Document Classification, Text categorisation, Emotion Recognition/Generation, Lexicon, lexical database |
Full paper |
Interpreting SentiWordNet for Opinion Classification |
Slides |
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| Bibtex | @InProceedings{SAGGION10.354, author = {Horacio Saggion and Adam Funk}, title = {Interpreting SentiWordNet for Opinion Classification}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |