Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews
This addresses the need for automated summarization of user reviews to assess software quality for companies, but it is incremental as it applies an existing method (LSA) to a new domain.
The paper tackled the problem of automatically categorizing user reviews into Quality in Use indicators (effectiveness, efficiency, freedom from risk) using Latent Semantic Analysis, achieving an average F-measure of 0.3627 in a preliminary study.
The paper describes a novel approach to categorize users' reviews according to the three Quality in Use (QU) indicators defined in ISO: effectiveness, efficiency and freedom from risk. With the tremendous amount of reviews published each day, there is a need to automatically summarize user reviews to inform us if any of the software able to meet requirement of a company according to the quality requirements. We implemented the method of Latent Semantic Analysis (LSA) and its subspace to predict QU indicators. We build a reduced dimensionality universal semantic space from Information System journals and Amazon reviews. Next, we projected set of indicators' measurement scales into the universal semantic space and represent them as subspace. In the subspace, we can map similar measurement scales to the unseen reviews and predict the QU indicators. Our preliminary study able to obtain the average of F-measure, 0.3627.