Contextual sentiment analysis for social media genres

The lexicon-based approaches to opinion mining involve the extraction of term polarities from sentiment lexicons and the aggregation of such scores to predict the overall sentiment of a piece of text. It is typically preferred where sentiment labelled data is difficult to obtain or algorithm robustness across different domains is essential. A major challenge for this approach is accounting for the semantic gap between prior polarities of terms captured by a lexicon and the terms' polarities in a specific context (contextual polarity). This is further exacerbated by the fact that a term's contextual polarity also depends on domains or genres in which it appears. In this paper, we introduce SmartSA, a lexicon-based sentiment classification system for social media genres which integrates strategies to capture contextual polarity from two perspectives: the interaction of terms with their textual neighbourhood (local context) and text genre (global context). We introduce an approach to hybridise a general purpose lexicon, SentiWordNet, with genre-specific vocabulary and sentiment. Evaluation results from diverse social media show that our strategies to account for local and global contexts significantly improve sentiment classification, and are complementary in combination. Our system also performed significantly better than a state-of-the-art sentiment classification system for social media, SentiStrength.