This paper describes the UNIBA team participation in the SENTIPOLC task at EVALITA 2014. We propose a supervised approach relying on keyword, lexicon and micro-blogging features as well as representation of tweets in a word space. Our system ranked 1st in both the subjectivity and polarity detection subtasks. As a further contribution, we participated in the unconstrained run, investigating the use of co-training to automatically enrich the labelled training set.
UNIBA at EVALITA 2014-SENTIPOLC Task: Predicting tweet sentiment polarity combining micro-blogging, lexicon and semantic features
BASILE, PIERPAOLO;NOVIELLI, NICOLE
2014-01-01
Abstract
This paper describes the UNIBA team participation in the SENTIPOLC task at EVALITA 2014. We propose a supervised approach relying on keyword, lexicon and micro-blogging features as well as representation of tweets in a word space. Our system ranked 1st in both the subjectivity and polarity detection subtasks. As a further contribution, we participated in the unconstrained run, investigating the use of co-training to automatically enrich the labelled training set.File in questo prodotto:
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