A gold mine of product aspects : analyzing Amazon consumer reviews using text mining
Online shoppers rely on product reviews when making purchase decisions. This is because where listed information in e-commerce contexts is often inadequate, consumer-generated reviews are a ‘gold mine’ of helpful product information for decision-making. Faced with hundreds, possibly thousands of rev...
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2022
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sg-ntu-dr.10356-1558562023-03-05T16:15:15Z A gold mine of product aspects : analyzing Amazon consumer reviews using text mining Tay, Elizabeth Ka-Yin Quek, Ching Yee Poong Oh Wee Kim Wee School of Communication and Information poongoh@ntu.edu.sg Social sciences::Communication::Communication theories and models Online shoppers rely on product reviews when making purchase decisions. This is because where listed information in e-commerce contexts is often inadequate, consumer-generated reviews are a ‘gold mine’ of helpful product information for decision-making. Faced with hundreds, possibly thousands of reviews per product listing, what exactly do consumers seek out when reading reviews? Addressing this question benefits both retailers and consumers through offering the former more effective strategies that can help the latter make better informed choices. The present study adopts topic modeling to extract key product aspects in reviews and performs a series of hierarchical regression analyses to examine how the various factors influence perceived review helpfulness. Subcategory topics were common across all four product categories, while topics related to observable product features and subjective product evaluation were only relevant to experience goods and high-involvement goods, respectively. The mixed effects of review, review author, and product listing characteristics, as well as extracted key topics on review helpfulness call for more in-depth investigation into online consumer behavior, particularly their motivations and how they affect product evaluation processes. The major findings of the current study are expected to inform e-commerce platform improvements that benefit both retailers and consumers. Bachelor of Communication Studies 2022-03-24T02:09:13Z 2022-03-24T02:09:13Z 2022 Final Year Project (FYP) Tay, E. K. & Quek, C. Y. (2022). A gold mine of product aspects : analyzing Amazon consumer reviews using text mining. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155856 https://hdl.handle.net/10356/155856 en CS21003 application/pdf Nanyang Technological University |
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Social sciences::Communication::Communication theories and models Tay, Elizabeth Ka-Yin Quek, Ching Yee A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
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Online shoppers rely on product reviews when making purchase decisions. This is because where listed information in e-commerce contexts is often inadequate, consumer-generated reviews are a ‘gold mine’ of helpful product information for decision-making. Faced with hundreds, possibly thousands of reviews per product listing, what exactly do consumers seek out when reading reviews? Addressing this question benefits both retailers and consumers through offering the former more effective strategies that can help the latter make better informed choices. The present study adopts topic modeling to extract key product aspects in reviews and performs a series of hierarchical regression analyses to examine how the various factors influence perceived review helpfulness. Subcategory topics were common across all four product categories, while topics related to observable product features and subjective product evaluation were only relevant to experience goods and high-involvement goods, respectively. The mixed effects of review, review author, and product listing characteristics, as well as extracted key topics on review helpfulness call for more in-depth investigation into online consumer behavior, particularly their motivations and how they affect product evaluation processes. The major findings of the current study are expected to inform e-commerce platform improvements that benefit both retailers and consumers. |
author2 |
Poong Oh |
author_facet |
Poong Oh Tay, Elizabeth Ka-Yin Quek, Ching Yee |
format |
Final Year Project |
author |
Tay, Elizabeth Ka-Yin Quek, Ching Yee |
author_sort |
Tay, Elizabeth Ka-Yin |
title |
A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
title_short |
A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
title_full |
A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
title_fullStr |
A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
title_full_unstemmed |
A gold mine of product aspects : analyzing Amazon consumer reviews using text mining |
title_sort |
gold mine of product aspects : analyzing amazon consumer reviews using text mining |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/155856 |
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1759857092864245760 |