Abstract
Over the last few years, the way people express their opinions has changed dramatically with the progress of social networks, web communities, blogs, wikis, and other online collaborative media. Now, people buy a product and express their opinion in social media so that other people can acquire knowledge about that product before they proceed to buy it. On the other hand, for the companies it has become necessary to keep track of the public opinions on their products to achieve customer satisfaction. Therefore, nowadays opinion mining is a routine task for every company for developing a widely acceptable product or providing satisfactory service. Concept-based opinion mining is a new area of research. The key parts of this research involve extraction of concepts from the text, determining product aspects, and identifying sentiment associated with these aspects. In this paper, we address each one of these tasks using a novel approach that takes text as input and use dependency parse tree-based rules to extract concepts and aspects and identify the associated sentiment. On the benchmark datasets, our method outperforms all existing state-of-the-art systems.
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Notes
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We used the Stanford Dependency parser, http://nlp.stanford.edu/software/lex- parser.shtml.
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References
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Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L. (2014). Dependency Tree-Based Rules for Concept-Level Aspect-Based Sentiment Analysis. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_5
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DOI: https://doi.org/10.1007/978-3-319-12024-9_5
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