CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis

Tan, Wendy Wei Syn and Bong, Chih How (2016) CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis. Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015. pp. 54-59. ISSN ISBN: 978-147998252-3

CompUXLSA A Computational Model in Predicting (abstract).pdf

Download (625kB) | Preview
[img] PDF

Download (64kB)
Official URL:


In this paper, we proposed a novel method (CompUXLSA) to predict user experience from reviews sentences using Latent Semantic Analysis (LSA). Human uses words to represent or express thoughts. The “word of mouth” could influence others especially through web and social media, which are the common communication tools today. We believe that reviews can be categorized according to user experiences since reviews are the thoughts and opinions from users after they have used certain products. In our works, we intend to mine and predict the user experience of expressed through reviews according to the five behavioral variables: Perceived Ease of Use, Perceived Usefulness, Affects towards Technology, Social Influence and Trust. We apply the state of the art method: Latent Semantic Analysis to build a semantic space and map review sentences to the most similar variable measurement items that adapted from Human Behavior Project to predict their experiences. Besides that, we also proposed a rule based template, SubEx to extract features of subject-experience from reviews to enhance the performance. Based on the results obtained, CompUXLSA had achieved average F-measure of 0.24.

Item Type: Article
Uncontrolled Keywords: Latent Semantic Analysis, opinion mining, user experience, research, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 16 Aug 2016 18:34
Last Modified: 17 Feb 2017 02:16

Actions (For repository members only: login required)

View Item View Item