The recent interests in Sentiment Analysis systems brought the attention on the definition of effective methods to detect opinions and sentiments in texts with a good accuracy. Many approaches that can be found in literature are based on hand-coded resources that model the prior polarity of words or multi-word expressions. The construction of such resources is in general expensive and coverage issues arise with respect to the multiplicity of linguistic phenomena of sentiment expressions. This paper presents an automatic method for deriving a largescale polarity lexicon based on Distributional Models of lexical semantics. Given a set of sentences annotated with polarity, we transfer the sentiment information from sentences to words. The set of annotated examples is derived from Twitter and the polarity assignment to sentences is derived by simple heuristics. The approach is mostly unsupervised, and the experimental evaluation carried out on two Sentiment Analysis tasks shows the benefits of the generated resource.
Castellucci, G., Croce, D., Basili, R. (2015). Acquiring a large scale polarity lexicon through unsupervised distributional methods. In Natural Language Processing and Information (pp.73-86). HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY : Springer Verlag [10.1007/978-3-319-19581-0_6].
Acquiring a large scale polarity lexicon through unsupervised distributional methods
Castellucci G.;Croce D.;Basili R.
2015-01-01
Abstract
The recent interests in Sentiment Analysis systems brought the attention on the definition of effective methods to detect opinions and sentiments in texts with a good accuracy. Many approaches that can be found in literature are based on hand-coded resources that model the prior polarity of words or multi-word expressions. The construction of such resources is in general expensive and coverage issues arise with respect to the multiplicity of linguistic phenomena of sentiment expressions. This paper presents an automatic method for deriving a largescale polarity lexicon based on Distributional Models of lexical semantics. Given a set of sentences annotated with polarity, we transfer the sentiment information from sentences to words. The set of annotated examples is derived from Twitter and the polarity assignment to sentences is derived by simple heuristics. The approach is mostly unsupervised, and the experimental evaluation carried out on two Sentiment Analysis tasks shows the benefits of the generated resource.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
