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The continued growth of users has generated a huge number of data in Internet, thus leading to the importance of developing new models for obtaining relevant information from this data. In this paper we aim to detect the polarity of user reviews. The purpose of this paper is to identify aspects and sentiments expressed for each aspect. We propose an unsupervised model for the identification of lexical and syntactic features for the detection of aspects and the classification of the sentiment expressed in user reviews into three features: positive, 171 Research in Computing Science 115 (2016) pp. 171–180; rec. 2016-04-22; acc. 2016-05-18 negative and neutral. Some features as the following ones are employed: part-of-speech tags, semantic similarity among words and co-ocurrence.

Análisis de sentimientos basado en aspectos: un modelo para identificar la polaridad de críticas de usuario