@inproceedings{perez-rosas-etal-2012-learning,
    title = "Learning Sentiment Lexicons in {S}panish",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Banea, Carmen  and
      Mihalcea, Rada",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Do{\u{g}}an, Mehmet U{\u{g}}ur  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/L12-1645/",
    pages = "3077--3081",
    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."
}