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}
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