Online-to-offline/offline-to-online (O2O) business models have attracted lots of enterprisers to enter this market. In such a fast-growing competition, some studies indicated that lack of trust will bring a great damage to O2O business. Related works already confirm trust is the key factor to the success of O2O. Besides, social media has been changing the way providers communicate with consumers. Negative comments in social media will decrease the consumers’ trust to O2O companies and platforms. Available O2O studies are almost always conducted by means of questionnaires or interviews, which cannot provide immediate customer response and require a lot of manpower and time. Since online reviews are the main information source for consumers. Therefore, this study presented a text mining-based scheme which uses text mining technique to find important factors from online electronic word-of-mouth, to replace the traditional questionnaire survey method of collecting data. Two feature selection methods, Support Vector Machines Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator have employed to select important factors that affect O2O trust. We also evaluate the performance of extracted feature subsets by Support Vector Machines. The findings can be referenced for O2O market enterprises to carefully response customers’ comments to avoid hurting customers’ trust and improve service quality.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Geometry and Topology