Enhanced bag-of-words model for phrase-level sentiment analysis

We propose a novel rule-based model to incorporate contextual information and effect of negation that enhances the performance of sentiment classification performed using bag-of-words models. We employed morphological analysis in feature extraction to ensure feature vector contains only opinionated words in a textual review. Also it reduces the dimensionality of feature vector and, eventually improves the efficiency of the classification algorithm. Further, we consider grammatical relationships to incorporate the context of adjectives and scope of negations within a phrase, to the feature vector. This enables our model to capture contextual polarity of adjectives and impact of negation words. For the morphological analysis we mainly employ Part Of Speech taggers (POS taggers) and grammatical relationships which are obtained using typed dependency parsers. By using dependency-based rules, we relax the conditional independent assumption of bag-of-words models by way of combining adjectives and negations to identified target words and, hence obtain a sentiment classification accuracy that significantly better than baseline performance.