Discovering User Experience Variables from Textusing Computational Semantics Approaches

Nielson Norman group defined user experience as, “all aspects of the end-user’s interaction with the company, its services, and its products”. Many researchers have investigated what criteria ensures good user experience. With the vast development in information technology, we could easily access...

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Main Author:	Tan, Wendy Wei Syn
Format:	Thesis
Language:	English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/10764/1/Wendy%20T.pdf
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id	my-unimas-ir.10764
record_format	uketd_dc
spelling	my-unimas-ir.107642023-08-24T02:04:43Z Discovering User Experience Variables from Textusing Computational Semantics Approaches 2015 Tan, Wendy Wei Syn T Technology (General) Nielson Norman group defined user experience as, “all aspects of the end-user’s interaction with the company, its services, and its products”. Many researchers have investigated what criteria ensures good user experience. With the vast development in information technology, we could easily access to user generated data such as reviews that explain user experiences. However, mountainous of reviews provide too much information and at the same time contain noises. These motivate this study to present a novel solution to automatically analyze reviews and predict the underlying user experiences. We believe that this solution provides insight into the behavioral aspects of those reviews where most of the time, we cannot observed them directly. In our study, we have proposed a Computational Model for User Experience (CompUX) that able to predict user experiences from reviews. We have choosen five main user experiences: Perceived Ease of Use, Perceived Usefulness, Affects towards Technology, Social Influence, and Trust. We have created an UX semantic space to learn the semantic meaning relationship of words and documents by incorporating the state of the art distributional semantic models: Latent Semantic Analysis and Paragraph Vector as part of the CompUX. Next, by mapping reviews to their semantically similar measurement items (derived from behavioral science) using the UX semantic space, we could infer user experiences from reviews. Based on the results obtained, the model performed better than random prediction and we were able to achieve macro average F-Measure of 0.31. Universiti Malaysia Sarawak, (UNIMAS) 2015 Thesis http://ir.unimas.my/id/eprint/10764/ http://ir.unimas.my/id/eprint/10764/1/Wendy%20T.pdf text en validuser masters Universiti Malaysia Sarawak, (UNIMAS) Faculty of Computer Science and Information Technology.
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Tan, Wendy Wei Syn Discovering User Experience Variables from Textusing Computational Semantics Approaches
description	Nielson Norman group defined user experience as, “all aspects of the end-user’s interaction with the company, its services, and its products”. Many researchers have investigated what criteria ensures good user experience. With the vast development in information technology, we could easily access to user generated data such as reviews that explain user experiences. However, mountainous of reviews provide too much information and at the same time contain noises. These motivate this study to present a novel solution to automatically analyze reviews and predict the underlying user experiences. We believe that this solution provides insight into the behavioral aspects of those reviews where most of the time, we cannot observed them directly. In our study, we have proposed a Computational Model for User Experience (CompUX) that able to predict user experiences from reviews. We have choosen five main user experiences: Perceived Ease of Use, Perceived Usefulness, Affects towards Technology, Social Influence, and Trust. We have created an UX semantic space to learn the semantic meaning relationship of words and documents by incorporating the state of the art distributional semantic models: Latent Semantic Analysis and Paragraph Vector as part of the CompUX. Next, by mapping reviews to their semantically similar measurement items (derived from behavioral science) using the UX semantic space, we could infer user experiences from reviews. Based on the results obtained, the model performed better than random prediction and we were able to achieve macro average F-Measure of 0.31.
format	Thesis
qualification_level	Master's degree
author	Tan, Wendy Wei Syn
author_facet	Tan, Wendy Wei Syn
author_sort	Tan, Wendy Wei Syn
title	Discovering User Experience Variables from Textusing Computational Semantics Approaches
title_short	Discovering User Experience Variables from Textusing Computational Semantics Approaches
title_full	Discovering User Experience Variables from Textusing Computational Semantics Approaches
title_fullStr	Discovering User Experience Variables from Textusing Computational Semantics Approaches
title_full_unstemmed	Discovering User Experience Variables from Textusing Computational Semantics Approaches
title_sort	discovering user experience variables from textusing computational semantics approaches
granting_institution	Universiti Malaysia Sarawak, (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology.
publishDate	2015
url	http://ir.unimas.my/id/eprint/10764/1/Wendy%20T.pdf
_version_	1783728071529988096

Discovering User Experience Variables from Textusing Computational Semantics Approaches

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