An Enhanced Semantic Tree Kernel for Sentiment Polarity Classification

Abstract

Sentiment analysis has gained a lot of attention in recent years, mainly due to the many practical applications it supports and a growing demand for such applications. This growing demand is supported by an increasing amount and availability of opinionated online information, mainly due to the proliferation and popularity of social media. The majority of work in sentiment analysis considers the polarity of word terms rather than the polarity of specific senses of the word in context. However there has been an increased effort in distinguishing between different senses of a word as well as their different opinion-related properties. Syntactic parse trees are a widely used natural language processing construct that has been effectively employed for text classification tasks. This paper proposes a novel methodology for extending syntactic parse trees, based on word sense disambiguation and context specific opinion-related features. We evaluate the methodology on three publicly available corpuses, by employing the sub-set tree kernel as a similarity function in a support vector machine. We also evaluate the effectiveness of several publicly available sense specific sentiment lexicons. Experimental results show that all our extended parse tree representations surpass the baseline performance for every measure and across all corpuses, and compared well to other state-of-the-art techniques.