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.
Highlights
Improvements in information and communication technologies break down geographical boundaries, allowing for faster connection and communication worldwide [1]
Enterprises are focusing on customer online reviews to support their decision-making process, such as risk management, sale prediction, market intelligence, new product design, trend prediction, advertisement placement, threats from competitors and benchmarking [12,13,14,15,16]
Feature frequency, semantic polarities and ratings are used for feature ranking to enhance consumers to consumer communication [41,42,43,44,45,46]
Summary
Improvements in information and communication technologies break down geographical boundaries, allowing for faster connection and communication worldwide [1]. Users need to gravitate through a host of reviews to learn about features that need to be considered before making a purchase due to the varying quality, enormous availability, distributed nature, voluminosity, heterogeneity, and multi-dimensionality of online reviews As a result, it is a time-consuming and tedious task to analyze and summarize online reviews to get information about competing features between products of different brands [23,24]. Feature frequency, semantic polarities and ratings are used for feature ranking to enhance consumers to consumer communication [41,42,43,44,45,46] These methods overlook important factors that can improve feature ranking by focusing on (i) opinion strength, such as how positive or negative an opinion word is, (ii) quality of reviews, and (iii) consideration of user preferences.
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