Dependency tree-based rules for concept-level aspect-based sentiment analysis

Poria, Soujanya; Ofek, Nir; Gelbukh, Alexander; Hussain, Amir; Rokach, Lior

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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: SemWebEval 2014 at ESWC 2014
• Start Date: May 25, 2014
• End Date: May 29, 2014
• Online Publication Date: Oct 4, 2014
• Publication Date: 2014
• Deposit Date: Sep 26, 2019
• Publisher: Springer
• Pages: 41-47
• Series Title: Communications in Computer and Information Science
• Series Number: 475
• Series ISSN: 1865-0937
• Book Title: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers
• ISBN: 9783319120232
• DOI: https://doi.org/10.1007/978-3-319-12024-9_5
• Keywords: concept-based opinion mining; dependency tree-based rules; sentiment analysis
• Public URL: http://researchrepository.napier.ac.uk/Output/1793029