Users’ Sentiment Analysis of Shopping Websites Based on Online Reviews

With the rapid development of online shopping, how to explore the value of online reviews, so as to give full play to their role in potential users’ purchasing decisions. Based on text mining and quantitative analysis, this paper studies the sentiment analysis of online reviews on B2C shopping website. The main attributes of commodity or service are extracted based on the order of word frequency in the online reviews. Text analysis method is used to judge the relationship between attributes of commodity or service and its emotional words. The fine-grained sentimental polarity and intensity of attributes are identified to analyze users’ concerns and preferences. The research shows that users pay more attention to the configuration and after-sales service of mobile, and have a positive sentimental orientation to most of attributes, especially unlocking function, hand feeling attribute and logistics service; and have a neutral sentimental orientation towards the attributes of battery and memory, and a negative sentimental orientation towards the membrane of mobile phone. The results can provide a reference for consumers to make purchasing decisions, for enterprises to improve product quality, and for shopping platform to optimize service.