Aspect extraction from product reviews using category hierarchy information
Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child...
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sg-smu-ink.sis_research-48052017-10-30T05:49:34Z Aspect extraction from product reviews using category hierarchy information YANG, Yifeng CHEN CEN, QIU, Minghui BAO, Forrest Sheng Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of subcategories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10%. 2017-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3803 info:doi/10.18653/v1/E17-2107 https://ink.library.smu.edu.sg/context/sis_research/article/4805/viewcontent/E17_2107.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computational linguistics Linguistics Computational Engineering Databases and Information Systems |
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Computational linguistics Linguistics Computational Engineering Databases and Information Systems YANG, Yifeng CHEN CEN, QIU, Minghui BAO, Forrest Sheng Aspect extraction from product reviews using category hierarchy information |
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Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of subcategories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10%. |
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text |
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YANG, Yifeng CHEN CEN, QIU, Minghui BAO, Forrest Sheng |
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YANG, Yifeng CHEN CEN, QIU, Minghui BAO, Forrest Sheng |
author_sort |
YANG, Yifeng |
title |
Aspect extraction from product reviews using category hierarchy information |
title_short |
Aspect extraction from product reviews using category hierarchy information |
title_full |
Aspect extraction from product reviews using category hierarchy information |
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Aspect extraction from product reviews using category hierarchy information |
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Aspect extraction from product reviews using category hierarchy information |
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aspect extraction from product reviews using category hierarchy information |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3803 https://ink.library.smu.edu.sg/context/sis_research/article/4805/viewcontent/E17_2107.pdf |
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