Multi-Attribute Online Decision-Making Driven by Opinion Mining

Abstract

With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

Keywords

• opinion mining
• opinion visualization
• sentiment analysis
• feature ranking
• review quality evaluation