Research on Sentiment Analysis of E-commerce Live Comments based on Text Mining

Wei Xiong; Yue Zuo; Menglin Zhang; Chenxi Zhang; Caiqing Guo

doi:10.54097/c2WoFCB2

Authors

Wei Xiong
Yue Zuo
Menglin Zhang
Chenxi Zhang
Caiqing Guo

DOI:

https://doi.org/10.54097/c2WoFCB2

Keywords:

Text Mining, E-commerce Live Broadcast, Sentiment Analysis

Abstract

With the rapid rise of live e-commerce in the field of e-commerce, the rich emotional information generated by users in live comments has become an important research object. This study takes e-commerce live comments as samples, uses text mining technology combined with machine learning methods, builds sentiment dictionary, trains sentiment analysis model and uses SnowNLP library for sentiment analysis, and deeply digs users' emotional tendency towards goods, services and shopping experience. For negative comments, word cloud is generated through word segmentation and word frequency calculation to visually show users' dissatisfaction and provide substantial support for updating the emotional dictionary. Through this comprehensive analysis, it provides a deeper user insight for e-commerce enterprises, and provides strong support for operational decisions and service optimization. Future research can focus on the application of deep learning techniques in sentiment analysis to further improve model accuracy and adaptability.

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