Abstract
When a researcher wants to implement questionnaire survey, they usually spend much time and efforts to questionnaire delivery, questionnaire collection and questionnaire analysis. In order to enhance efficiency and effectiveness in questionnaire survey, this research develops an automatic questionnaire survey model. In the proposed model, the questions in the questionnaires can be converted into structured components and the element characteristics can be obtained from the related online message. By using the Artificial Neural Network method, the relationship model between message and questionnaire questions can be derived and the derived model can be used to obtain the answers to the questions from the message highly related to the questions. Moreover, distributions of the answers can be visually depicted. By utilizing the proposed methodology, researchers can efficiently realize the results about their questionnaire survey from the online population. By utilizing the model, this research develops a platform for automatic questionnaire survey and analysis. Moreover, real-world questions and message, Yahoo Answers, are used to evaluate the feasibility and performance of the proposed model and platform. As a result question answers can be effectively obtained from the message highly related to the questions. The experimental results show that our method and platform can help people quickly and effectively accomplish questionnaire survey.