Build sentiment classification prediction model for O2O service

With the rapid development of information and communication technology, O2O (Online to Offline) business model has attracted lots of attentions for enterprises. In such a fast-growing environment, some studies indicated that lack of trust will bring a great damage to O2O business. Besides, some published works pointed out those negative comments in social communities will decrease the consumer's trust to O2O companies and platforms. So, it is necessary for enterprises to understand the important factors that affect consumers' sentiment of textual reviews. Therefore, this study aims to build prediction models by using Support Vector Machines Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO), respectively. We do not only attempt to build sentiment classification models, but also to find the important factors that affect the sentiments of comments. The findings can be references for O2O market enterprises to carefully answer customers' comments to improve customers' trust and service quality.