Abstract: The application of neural network into speech emotion analysis and text appeal classification in power service is a novel algorithm. Compared with the traditional method, the method of feature engineering is avoided, no human feature selection is needed, and more robust features can be learned. Inspired by the multi-task learning, this paper designs a multi-modal multi-task model, which can manage two different modes data of voice and text, on the one hand, it uses emotional analysis to evaluate the power supplying service, on the other hand, it introduces the classification of user demands, using similar tasks to improve the performance of a single task. The experiments show that the result in the single task of the model in this paper is close to the result of the best model.

Key words: multi-model, multi-task, sentiment analysis, appeal classification, neural network