Entropy Weight-TOPSIS Method Considered Text Information with an Application in E-Commerce

Ailin Liang (), Xueqin Huang, Tianyu Xie, Liangyan Tao and Yeqing Guan
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Ailin Liang: Nanjing University of Aeronautics and Astronautics
Xueqin Huang: Nanjing University of Aeronautics and Astronautics
Tianyu Xie: Nanjing University of Aeronautics and Astronautics
Liangyan Tao: Nanjing University of Aeronautics and Astronautics
Yeqing Guan: Nanjing University of Aeronautics and Astronautics

A chapter in AI and Analytics for Public Health, 2022, pp 137-148 from Springer

Abstract: Abstract With the rapid development of e-commerce, enterprises are no longer satisfied with making use of traditional data to make decisions on product selling points and marketing strategies. They hope to make decisions more reasonably and rapidly with the help of big data, especially they want to combine a large amount of comment text information by customers. Therefore, a new method is needed to solve this problem. This paper takes the data of pacifier, microwave oven and hair dryer sold by Amazon in recent 10 years as an actual case, and proposes a data preprocessing method for this kind of problem. Based on Feature Engineering and LDA, an indicators system considering the text information of comments is established. Then, the entropy weight TOPSIS algorithm is used to sort the products, and the top products are selected for further analysis. Finally, by analyzing the comment text and related features of the top ranked products, the reasons for their marketing success are extracted to assist enterprises in making relevant decisions, including but not limited to product quality characteristics, pricing, marketing strategies.

Keywords: E-commerce; Sentiment analysis; LDA; Entropy weight TOPSIS (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-75166-1_8

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DOI: 10.1007/978-3-030-75166-1_8

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