Study on the classification problem of the coping stances in the Satir model based on machine learning

ABSTRACT This paper applies machine learning technology to the Satir theory model and intelligently classifies the communication stances of the second layer according to the language and behaviour information of the first layer. We arranged a large number of dialogical language materials from a TV interview programme and used the ICTCLAS Chinese word segmentation system to create a ‘psychological consultation database’. We construct the word training set by part of making use of speech filtering and text word vectorisation, and construct the semantic training set by annotating the original data with the Satir model. These two sets form the Satir communication posture classification training set. Experimental results show that the success rate of classification of four inconsistent coping stances reached 70.37%, 75.92%, 83.33%, and 77.78%.