1 research outputs found
Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts
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 lexiconbased
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.SmartSeg project, which is co-funded through Portugal 2020’s "R&D Incentive System - Individual Projects" program, grant number "POCI-01-0247-FEDER-011192