Consumer Online Review Content Mining and Emotion Analysis Under the Background of Live Streaming E-Commerce
- DOI
- 10.2991/978-94-6463-570-6_35How to use a DOI?
- Keywords
- electricity supplier logistics; LDA topic model; live-streaming e-commerce; agricultural products; SnowNLP sentiment analysis
- Abstract
Online reviews of products in the mode of live e-commerce contain important factors that affect consumers’ purchasing decisions. The content of agricultural product reviews on the Tiktok platform was collected by Python web crawler, and 23557 agricultural product reviews were analyzed by word frequency analysis, network co-occurrence of high-frequency word meanings, LDA theme model, SnowNLP sentiment analysis and other methods. The study found that product quality, express packaging, anchor recommendation and logistics speed are the key factors affecting consumers’ online purchase decisions under the live streaming e-commerce model. On this basis, strengthening product quality, improving express packaging, improving the professionalism of anchors, and ensuring logistics speed are important measures to improve consumers’ desire to buy, and are of great significance to promote the sustainable development of the agricultural live e-commerce industry.
- Copyright
- © 2024 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.
Cite this article
TY - CONF AU - Fang Zhang AU - Fengxiao Li PY - 2024 DA - 2024/11/22 TI - Consumer Online Review Content Mining and Emotion Analysis Under the Background of Live Streaming E-Commerce BT - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024) PB - Atlantis Press SP - 337 EP - 346 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-570-6_35 DO - 10.2991/978-94-6463-570-6_35 ID - Zhang2024 ER -