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Data Mining of e-commerce agricultural product reviews Based on LDA topic Model

Published: 17 April 2024 Publication History

Abstract

At present, the role of e-commerce platform in the sales of agricultural products has become increasingly prominent, and the data of e-commerce user reviews are growing explosively. In-depth mining of these data reflecting consumers 'emotional tendencies is of great significance to help consumers make purchase decisions and businesses optimize products and services. Taking the review data of Hainan Qiaotou sweet potato on Jingdong Mall as an example, this paper uses word frequency statistical analysis and other methods to analyze the data, and further mines the valuable information hidden in the data with the help of LDA topic model, which provides a reference for the e-commerce enterprises of agricultural products to develop a reasonable and effective operation plan.

References

[1]
Pang B, Lee L, Vaithyanathan S. Thumbs Up: Sentiment Classification Using Machine Learning Techniques [C]//Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Stroudsburg: Association for Computational Linguistics Press, 2002: 79–86.
[2]
Hu M, Liu B. Mining and Summarizing Customer Reviews [C]//Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, 2004: 168–177.
[3]
Tago K, Jin Q. Influence analysis of emotional behaviors and user relationships based on Twitter data [J]. Tsinghua Science & Technology, 2018, 23(1): 104-113.
[4]
Ruan Guang-chi. Research on topic discovery of network reviews based on LDA [J]. Journal of Intelligence, 2014, 33(03):161-164.
[5]
Wang Tao, Li Ming. Research on comment text mining Based on lda model and Semantic Network [J]. Journal of Chongqing Technology and Business University (Natural Science (Ed.), 2019, 36(04): 9-16.
[6]
Wu Jiang, Zhou Lusha, Liu Guanjun, Research on topic mining of online reviews of wearable devices based on LDA [J]. Information Resource Journal of Resource Management, 2017, 7(3): 24-33.
[7]
Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation [J]. Journal of machine Learning research, 2003, 3(Jan): 993-1022.
[8]
Zhang Yu-yan, Jiang Ying-zi. An analysis on the market prospect of community group-buying in Xuzhou area [J]. Rural Economy and Science and Technology, 2021, 32(20): 132-134.

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EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2024

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EITCE 2023

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Overall Acceptance Rate 508 of 972 submissions, 52%

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