Affiliations: [a] Department of Computer Science, South Asian University, Delhi, India
| [b] Department of Computer Science, Jamia Millia Islamia (A Central University), Delhi, India
Correspondence:
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Corresponding author. Jahiruddin, Department of Computer Science, Jamia Millia Islamia (A Central University), Delhi, India. E-mail: [email protected].
Abstract: Due to proliferation of competitive online Business-to-Consumer (B2C) models, it is becoming a challenging task for new users to choose best products, based on existing users’ reviews residing on different e-commerce websites. On analysis, it is found that the opinions of the existing customers play an important role for new customers in making appropriate purchase decisions. Though there are some online websites that provide aggregation of basic product information from multiple sources, there is a negligible research effort in the direction of opinion-based product ranking. In this paper, we propose an Opinion-based Multi-Criteria Ranking (OMCR) approach, which amalgamates structural and content-based features of review documents to rank different alternatives of the online products. It uses a total number of five features based on reviews’ meta-data and contents to rank different alternatives using multi-criteria decision making approaches. OMCR also incorporates a sentiment analysis and visualization approach to determine sentiment polarity values and visualize them in a comprehendible manner. Experiments are conducted over two different real datasets, and efficacy of OMCR is assessed using set intersection method, which is generally used to compare two ranked lists in terms of their overlapping score.
