Extracting, Ranking, and Evaluating Quality Features of Web Services through User Review Sentiment Analysis

Quality of Service (QoS) has become a standard way of evaluating web services and selecting the one that suites user interests the best. Traditional methods adopt a fixed set of QoS parameters and typical ones include response time, fee, and availability. There currently lacks an effective way of identifying quality features that users are actually interested in when choosing a service. Meanwhile, the traditional way of collecting QoS values relies on either public information released by service providers or test results from repeatedly invoking a service. Therefore, the values can be heavily affected by authenticity of the provider offered information or the quality/configuration of the test code/environment. As a result, existing QoS evaluation methods are not applicable to subject features, such as usability and affordability, where the values depend on user personal judgement. In this paper, we propose a novel approach to extracting domain-related QoS features, ranking those features based on their interestingness, evaluating the value of these features through sentiment analysis on user reviews. More specifically, we leverage natural language processing techniques and machine learning approaches to identify top QoS features that users are interested in and simultaneously learn their sentiment orientation towards those features. We model the problem as sentiment classification, where relevant terms in a review are modeled as features that determine whether a review is positive or negative. Logistic regression is used so that the impact of these terms are learned simultaneously when the classifier is learned through a supervised learning process. The nontrivial terms are selected as the candidate QoS featured. A comprehensive experiment has been conducted on a real-world dataset and the result demonstrates the effectiveness of our approach.