CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis
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 commo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc
2016
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/13022/ http://ir.unimas.my/id/eprint/13022/1/CompUXLSA%20A%20Computational%20Model%20in%20Predicting%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13022/7/display.uri_view%3Dbasic%26eid%3D2-s2.0-84978910817%26origin%3Dresultslist |
| _version_ | 1848837323939119104 |
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| author | Tan, Wendy Wei Syn Bong, Chih How |
| author_facet | Tan, Wendy Wei Syn Bong, Chih How |
| author_sort | Tan, Wendy Wei Syn |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T06:37:50Z |
| format | Article |
| id | unimas-13022 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T06:37:50Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers Inc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-130222017-02-17T02:16:34Z http://ir.unimas.my/id/eprint/13022/ CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis Tan, Wendy Wei Syn Bong, Chih How T Technology (General) 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. Institute of Electrical and Electronics Engineers Inc 2016 Article PeerReviewed text en http://ir.unimas.my/id/eprint/13022/1/CompUXLSA%20A%20Computational%20Model%20in%20Predicting%20%28abstract%29.pdf text en http://ir.unimas.my/id/eprint/13022/7/display.uri_view%3Dbasic%26eid%3D2-s2.0-84978910817%26origin%3Dresultslist 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 http://ieeexplore.ieee.org/document/7482157/ DOI: 10.1109/ICCSCE.2015.7482157 |
| spellingShingle | T Technology (General) Tan, Wendy Wei Syn Bong, Chih How CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title | CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title_full | CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title_fullStr | CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title_full_unstemmed | CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title_short | CompUXLSA: A computational model in predicting user experience from reviews using Latent Semantic Analysis |
| title_sort | compuxlsa: a computational model in predicting user experience from reviews using latent semantic analysis |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/13022/ http://ir.unimas.my/id/eprint/13022/ http://ir.unimas.my/id/eprint/13022/ http://ir.unimas.my/id/eprint/13022/1/CompUXLSA%20A%20Computational%20Model%20in%20Predicting%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13022/7/display.uri_view%3Dbasic%26eid%3D2-s2.0-84978910817%26origin%3Dresultslist |