LDA Topic Mining of Customer Text Sentiment Analysis on the E-Commerce Platform

DOI

10.2991/978-94-6463-010-7_6How to use a DOI?

Keywords

Text Data Mining; Sentiment Analysis; LDA Models; User Reviews

Abstract

Collect the reviews left by users on the e-commerce platform for processing and analysis to understand users’ needs, opinions, and product strengths and weaknesses. The text review data on the Jingdong platform was collected through a Python crawler, which was pre-processed by word separation, lexical annotation, and deactivation, and the key information of the reviews was extracted using the LDA topic model for research and analysis. The analysis of the integrated theme and its high-frequency feature words will help to obtain the valuable content and emotional orientation of the text review data and derive the advantages and disadvantages of the product and the corresponding needs of the users, providing consumers, merchants, and regulators with references and suggestions for collection.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiahao Duan
PY  - 2022
DA  - 2022/12/02
TI  - LDA Topic Mining of Customer Text Sentiment Analysis on the E-Commerce Platform
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 38
EP  - 46
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_6
DO  - 10.2991/978-94-6463-010-7_6
ID  - Duan2022
ER  -