Improving online decision making process based on the ranking of user reviews and product features / Azra Shamim

• Main Author: Shamim, Azra
• Format: Thesis
• Published: 2015
• Subjects: QA75 Electronic computers. Computer science; T Technology (General)
• Online Access: http://studentsrepo.um.edu.my/5799/1/IMPROVING_ONLINE_DECISION_MAKING_PROCESS_BASED_ON_THE_RANKING_OF_USER_REVIEWS_AND_PRODUCT_FEATURES.pdf; http://studentsrepo.um.edu.my/5799/
• Institution: Universiti Malaya

The ubiquity of Web2.0 with the proliferation of blogs and social networks transformed the way people express their opinions about different entities, such as products and services. Online reviews have become a powerful source of information for customers and business that gauge customers’ purchase intentions and enterprise strategies. The amount of user generated content has grown at a fast pace that forces users to gravitate through a number of online reviews in order to get decision oriented information, which is time consuming and tedious job. Consequently, a new line of research ‘opinion mining’ has emerged. Opinion mining techniques can help to alleviate the problem of information overload in online reviews by analyzing, summarizing and presenting peoples’ opinions. Online reviews vary greatly in quality and it has become imperative to identify high quality reviews to enhance the decision making process. However, most of existing opinion mining techniques ignore the quality of reviews. Although some review quality evaluation approaches are discussed in the literature, however, the focus is not on users’ preferences. Feature-based opining mining is required to provide a detailed feature-based summary in order to satisfy users’ need. Different methods have been proposed in the literature which evaluate and rank product features. However, existing feature ranking methods utilized the overall user rating and semantic polarity to rank product features, and overlook opinion strength. In addition, the visualization of the opinion summary is orthogonal to review quality evaluation and feature ranking. Most of existing opinion visualizations present overall positive and negative semantic on each feature and are unable to reflect opinion-strength based summary. The objectives of this research work are to integrate high quality reviews and opinion strength in feature ranking and to present opinion-strength based summarization using a visualization technique. Existing factors for review ranking have been investigated and significant factors were assimilated in the proposed methods according to the users’ preferences. Similarly, current elements for feature ranking have been examined and were amalgamated with opinion strength in the proposed method. Seminars and an online web based questionnaire survey was conducted to get the users’ inclinations about opinion visualization to propose an opinion-strength based visualization. A feature based opinion mining system was developed based on proposed methods and experimental results on real life data sets show that integration of review and feature ranking with strength-based feature level summary can improve the decision making process.