Analysis of Customer Comment Data on E-commerce Platforms Based on RPA Robots

Abstract

This study aims to analyze customer review data on e-commerce platforms using RPA robots, specifically focusing on drum washing machines. The research involves collecting text comment data from JD Mall and implementing data cleaning, Chinese word segmentation, and stop word removal preprocessing. The research employs the ROSTCM6 to construct high-frequency words, line feature words, and a semantic network. In addition, the LOG-CONTROL-BLOCK incorporates feedback control during the trajectory correction process to build a record controller module for audit robot inspection trajectory correction. The RPA feedback correction algorithm achieves adaptive correction of inspection trajectory and error feedback tracking for audit robots. The study identifies three potential keywords and evaluates probabilities associated with positive and negative themes. This analysis aims to deepen the understanding of consumer’s positive emotions and complaints post-purchase. The findings lead to several suggestions for enhancing e-commerce sales strategies for drum washing machines. Through a comprehensive analysis of customer review data, this research contributes insights into consumer sentiments related to drum washing machines on e-commerce platforms. The results provide valuable information for optimizing e-commerce sales strategies, emphasizing the importance of addressing consumer concerns and preferences in the drum-washing machine market.