Combining lexicon and learning based approaches for concept-level sentiment analysis

Authors:
Andrius Mudinas;Dell Zhang;Mark Levene
Affiliations:
University of London, London, UK;University of London, London, UK;University of London, London, UK
Venue:
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Year:
2012

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Abstract

In this paper, we present the anatomy of pSenti --- a concept-level sentiment analysis system that seamlessly integrates into opinion mining lexicon-based and learning-based approaches. Compared with pure lexicon-based systems, it achieves significantly higher accuracy in sentiment polarity classification as well as sentiment strength detection. Compared with pure learning-based systems, it offers more structured and readable results with aspect-oriented explanation and justification, while being less sensitive to the writing style of text. Our extensive experiments on two real-world datasets (CNET software reviews and IMDB movie reviews) confirm the superiority of the proposed hybrid approach over state-of-the-art systems like SentiStrength.