Extraction of product defects and opinions from customer reviews by using text clustering and sentiment analysis
Extraction of product defects and opinions from customer reviews by using text clustering and sentiment analysis
The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.
4529-4534
Cataltas, Mustafa
441ca503-2bb3-4f1d-8523-af06e07460fc
Dogramaci, Sevcan
0749e4cd-33dd-4bce-90da-e32218901637
Yumusak, Semih
5a45f53d-7a3c-4e3d-93b1-bc83f7096f37
Oztoprak, Kasim
787fc90b-50e6-44b8-a64d-8c388c72e678
19 March 2021
Cataltas, Mustafa
441ca503-2bb3-4f1d-8523-af06e07460fc
Dogramaci, Sevcan
0749e4cd-33dd-4bce-90da-e32218901637
Yumusak, Semih
5a45f53d-7a3c-4e3d-93b1-bc83f7096f37
Oztoprak, Kasim
787fc90b-50e6-44b8-a64d-8c388c72e678
Cataltas, Mustafa, Dogramaci, Sevcan, Yumusak, Semih and Oztoprak, Kasim
(2021)
Extraction of product defects and opinions from customer reviews by using text clustering and sentiment analysis.
In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data).
IEEE.
.
(doi:10.1109/BigData50022.2020.9377851).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.
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Published date: 19 March 2021
Venue - Dates:
2020 IEEE International Conference on Big Data (Big Data), , Atlanta, United States, 2020-12-10 - 2020-12-13
Identifiers
Local EPrints ID: 478433
URI: http://eprints.soton.ac.uk/id/eprint/478433
PURE UUID: 5e2e572d-5c51-46fb-a002-6e211e9e7b4d
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Date deposited: 30 Jun 2023 16:52
Last modified: 17 Mar 2024 02:35
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Contributors
Author:
Mustafa Cataltas
Author:
Sevcan Dogramaci
Author:
Semih Yumusak
Author:
Kasim Oztoprak
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