Summary of the paper
| Title | Learning Sentiment Lexicons in Spanish |
|---|---|
| Authors | Veronica Perez-Rosas, Carmen Banea and Rada Mihalcea |
| Abstract | In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources. |
| Topics | Emotion Recognition/Generation, Multilinguality, Lexicon, lexical database |
Full paper  |
Learning Sentiment Lexicons in Spanish |
| Bibtex | @InProceedings{PEREZROSAS12.1081, author = {Veronica Perez-Rosas and Carmen Banea and Rada Mihalcea}, title = {Learning Sentiment Lexicons in Spanish}, 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} } |