A model for sentiment analysis based on ontology and cases

Abstract

This work intends to combine domain ontology with natural language processing techniques to identify the sentiment behind judgments aiming to provide an explanation for such polarization. Also, it intends to use the Case-Based Reasoning strategy in order to learn from past reasonings (polarizations) so they can be used in new polarizations. Some steps have been developed for treatment of negation, adequacy of sentiment lexicon for a domain and adaptation of ambiguous terms classification based on past ratings. Tests were developed in two distinct areas, digital cameras and movies, to justify the model evolution until its final proposal. It was observed that the accuracy obtained by the proposed model overcomes standard statistical approaches. These results demonstrate that the model contributes to the sentiment analysis area, both as a solution that provides high levels of accuracy, as well as the possibility to present the track to achieve a particular classification.