Paper: Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages

ACL ID E14-1010
Title Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper presents a simple, robust and (almost) unsupervised dictionary-based method, qwn-ppv (Q-WordNet as Person- alized PageRanking Vector) to automati- cally generate polarity lexicons. We show that qwn-ppv outperforms other automat- ically generated lexicons for the four ex- trinsic evaluations presented here. It also shows very competitive and robust results with respect to manually annotated ones. Results suggest that no single lexicon is best for every task and dataset and that the intrinsic evaluation of polarity lexicons is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv method allows to easily create quality po- larity lexicons whenever no domain-based annotated corpora are available for a given language.