Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts - Saias, Jose; Mourão, Mário; Oliveira, Eduardo (Transactions on Engineering and Computing Sciences, 2018)
Sentiment analysis is useful for identifying trends, or for discovering user preferences, which can later be applied to campaign targeting or recommendations. In this paper, we describe an approach to classify the sentiment polarity regarding aspects, and how this technique was used in a previous system, for short texts in Portuguese, giving it greater sensitivity to detail. Aspect extraction is done by locating candidates for aspect as expressions having a relationship with the entity and possibly some polarized term, through rules based on POS tags. For each aspect, the sentiment polarity is determined by a Maximum Entropy classifier, whose features depend on the entity mention, on the aspect and its support text, including negation detection, bigrams, POS tags, and sentiment lexicon-based polarity clues. For aspect sentiment, our classifier evaluation indicated a precision of 68% for the positive class and 73% for the negative class, with the dataset used in our research.