Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews

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 revi...

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Bibliographic Details
Published in:arXiv.org 2015-03
Main Authors: Wendy Tan Wei Syn, How, Bong Chih, Atoum, Issa
Format: Article
Language:English
Subjects:
Balances (scales)
Electronic commerce
Indicators
Semantic analysis
Semantics
Subspaces
Online Access:Get full text
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Summary: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.
ISSN:2331-8422