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 rev...
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| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | en |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/8454/1/Wendy%20Tan.pdf http://ir.unimas.my/id/eprint/8454/ http://www.researchgate.net/publication/268445135_Using_Latent_Semantic_Analysis_to_Identify_Quality_in_Use_%28QU%29_Indicators_from_User_Reviews |
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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 Fmeasure,
0.3627. |
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