Consumer reviews analysis on E-commerce platforms
In recent years, online shopping has become more dominant as a marketing channel. Consumers can interact with products in various ways online – view, purchase, return, comment, recommend etc. It enables a complete purchase cycle in a purely digital form, allowing the information to be more accessibl...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158784 |
| _version_ | 1826126260847247360 |
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| author | Lin, Lixian |
| author2 | Lihui Chen |
| author_facet | Lihui Chen Lin, Lixian |
| author_sort | Lin, Lixian |
| collection | NTU |
| description | In recent years, online shopping has become more dominant as a marketing channel. Consumers can interact with products in various ways online – view, purchase, return, comment, recommend etc. It enables a complete purchase cycle in a purely digital form, allowing the information to be more accessible and spreadable. To keep up with the market trends and consumer needs, brands view it as an opportunity to better understand consumer behaviors. Customer reviews on e-commerce platforms are the focus of analysis as they are public-accessible, diverse, and enormous.
This project targets on the largest consumer market in Asia – China and apply a machine learning method to analyze Chinese reviews from the top e-commerce platform Tmall.com. It aims to understand the sentiment from Chinese text by identifying aspects of interest mentioned in review and clustering into pre-defined groups.
In particular, an attention-based aspect extraction model is studied, implemented and tuned to fit a dataset of another language. A Chinese Beauty Corpus containing 200k Chinese reviews from mainstream makeup and skincare products across categories and brands is built to train the model. Language-specific pre-processing methods are studied and applied, following by the usage of word2vec model to generate meaningful embeddings for model to learn.
First part of this report focuses on background knowledge where related models and methods are studied. Model understanding and implementation are elaborated in the second part where method and algorithm are explained in detail. The last part shows experiment and result. |
| first_indexed | 2024-10-01T06:49:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158784 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:49:45Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1587842023-07-07T18:56:46Z Consumer reviews analysis on E-commerce platforms Lin, Lixian Lihui Chen School of Electrical and Electronic Engineering ELHCHEN@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence In recent years, online shopping has become more dominant as a marketing channel. Consumers can interact with products in various ways online – view, purchase, return, comment, recommend etc. It enables a complete purchase cycle in a purely digital form, allowing the information to be more accessible and spreadable. To keep up with the market trends and consumer needs, brands view it as an opportunity to better understand consumer behaviors. Customer reviews on e-commerce platforms are the focus of analysis as they are public-accessible, diverse, and enormous. This project targets on the largest consumer market in Asia – China and apply a machine learning method to analyze Chinese reviews from the top e-commerce platform Tmall.com. It aims to understand the sentiment from Chinese text by identifying aspects of interest mentioned in review and clustering into pre-defined groups. In particular, an attention-based aspect extraction model is studied, implemented and tuned to fit a dataset of another language. A Chinese Beauty Corpus containing 200k Chinese reviews from mainstream makeup and skincare products across categories and brands is built to train the model. Language-specific pre-processing methods are studied and applied, following by the usage of word2vec model to generate meaningful embeddings for model to learn. First part of this report focuses on background knowledge where related models and methods are studied. Model understanding and implementation are elaborated in the second part where method and algorithm are explained in detail. The last part shows experiment and result. Bachelor of Engineering (Information Engineering and Media) 2022-05-29T12:52:27Z 2022-05-29T12:52:27Z 2022 Final Year Project (FYP) Lin, L. (2022). Consumer reviews analysis on E-commerce platforms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158784 https://hdl.handle.net/10356/158784 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Lin, Lixian Consumer reviews analysis on E-commerce platforms |
| title | Consumer reviews analysis on E-commerce platforms |
| title_full | Consumer reviews analysis on E-commerce platforms |
| title_fullStr | Consumer reviews analysis on E-commerce platforms |
| title_full_unstemmed | Consumer reviews analysis on E-commerce platforms |
| title_short | Consumer reviews analysis on E-commerce platforms |
| title_sort | consumer reviews analysis on e commerce platforms |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/158784 |
| work_keys_str_mv | AT linlixian consumerreviewsanalysisonecommerceplatforms |