Product Reputation Evaluation Based on Multiple Web Sources

PhD student: 
External supervisors: 


M. ABDUL QADIR (Mohammad Ali Jinnah University - Pakistan)

Starting date: 
January 2013
Defense date: 
Friday 20 January 2017
Host institution: 
Other institution: 
Mohammad Ali Jinnah University - Pakistan

This thesis proposes methods and techniques for evaluating product reputation based on data available on the Web and to provide valuable information to customers and manufacturers for decision making.

These methods perform the following tasks:

1) extract product evaluation data from multiple Web sources

2) analyze product reviews in order to determine that whether opinions about product features in customer reviews are positive or negative,

3) computes different product reputation values while considering different evaluation criteria, and

4) finally the results are provided to customers and manufacturers in order to make decisions.

This thesis contributes in three main research areas i.e.

1) feature level sentiment analysis,

2) product reputation model and

3) multi-agent architecture.

First, a word sense disambiguation and negation handling methods are proposed in order to improve the performance of feature level sentiment analysis. Second, a novel mathematical model is proposed which computes several reputation values in order to evaluate product based on different criteria. Finally, multi-agent architecture for review analysis and product evaluation is proposed. Huge amount of the product evaluation data on the Web is in textual form (i.e. product reviews).

In order to analyze product reviews to evaluate product we propose a feature level sentiment analysis method which determines the opinions about different features of a product. A word sense disambiguation method is introduced which identify the sense of words according to the context while determining the polarity. In addition, a negation handling method is proposed which determine the sequence of words affected by different types of negations. The results show that both word sense disambiguation and negation handling methods improve the overall accuracy of feature level sentiment analysis. A multi-source product reputation model is proposed where informative, robust and strategy proof aggregation methods are introduced to compute different reputation values. Sources from which reviews are extracted may not be creditable hence a source credibility measuring method is proposed in order to avoid malicious web sources. In addition, suitable decay principles for product reputation are also introduced in order to reflect the newest opinions about product quickly. The model also considers several parameters such as reviewer expertise, rating trustworthiness, time span of ratings, reviewer age, sex and location in order to evaluate product in different ways. Different types of ratings (i.e. textual and numeric ratings) are considered to compute reputation values which increase the choices for customers and manufacturers to make decisions. The results show that the proposed model is robust, strategy proof, able to reflect recent opinions, and estimates true reputation values even if some ratings are false.

KEYWORDS: Traceability, Diagnosis, Product recall, Bayesian Networks