Data Mining of New Snack E-commerce Reviews Based on Text Sentiment Analysis and Latent Dirichlet Allocation Topic Model

DOI

10.2991/aebmr.k.200908.062How to use a DOI?

Keywords

e-commerce online reviews, text sentiment analysis, LDA topic model, Python, new snacks

Abstract

In the new retail era, in order to promote the development of new snack e-commerce enterprises, increase customer satisfaction with their products and services and the desire to repurchase, this article makes sentiment analysis on the 430,000 online reviews of new snack e-commerce as crawled online from the perspective of customers. Firstly, it makes an overall sentiment judgment on the online review texts of five stores. Secondly, it constructs the Latent Dirichlet Allocation (LDA) topic model, gets ten categories of keywords, analyzes the cluster analysis results and discusses for improving any consumer dissatisfaction point. Empirical analysis shows that in order to improve customer satisfaction and stickiness and attract more potential consumers, new snack e-commerce companies need to pay attention to optimizing brand reputation, product quality, service level, marketing strategy, and review information.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qian Yang
PY  - 2020
DA  - 2020/09/08
TI  - Data Mining of New Snack E-commerce Reviews Based on Text Sentiment Analysis and Latent Dirichlet Allocation Topic Model
BT  - Proceedings of the 3rd International Conference on Economy, Management and Entrepreneurship (ICOEME 2020)
PB  - Atlantis Press
SP  - 372
EP  - 378
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200908.062
DO  - 10.2991/aebmr.k.200908.062
ID  - Yang2020
ER  -