Sentence-Level Sentiment Polarity Classification Using a Linguistic Approach

Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1-score when compared to a Typed-dependency only approach.