Sentiment Analysis of Online Catering User Comments Based on Random Forest Feature Extraction
Yanqiu Liu, Fuming Ye · 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN) · 2022
With the development of the Internet economy, online ordering modes have gradually been widely accepted, and even become an indispensable part of the lives of office workers. But, the online catering platform has many difficulties, such as low entry threshold for merchants, no restriction of online catering industry standards, and unable to effectively and longterm supervise a large number of merchants. In order to realize the effective supervision of online catering and take into account the characteristics of Chinese, this paper first obtains the user generated content of catering businesses through the Internet and uses the Jieba word segmentation tool for Chinese word segmentation, then carries out data cleaning and uses the Random Forest model to screen out five features that have great impact, and finally uses the SVM, Decision Tree and AdaBoost algorithm for data modeling, prediction and analysis. The experimental results show the better performance of our method in online catering user comments data.