Unsupervised Word Polarity Tagging by Exploiting Continuous Word Representations

Aitor García Pablos, Montse Cuadros, German Rigau


Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polarity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatically generating domain adapted polarity lexicons employing continuous word representations, in particular the popular tool Word2Vec. First we show a qualitative evaluation of a small set of words, and then we show our results in the SemEval-2015 task 12 using the presented method.

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