The value orientation clustering analysis based on topic models in the social network environment

The amount of user-generated data is growing with the fast expansion of social networks and e-commerce platforms; so, how to recognise and evaluate user values from these enormous amounts becomes a crucial study issue. In this study, a hybrid technique framework based on the combination of LDA topic model and DBSCAN clustering algorithm is provided for effective analysis of user values. First, the LDA model mines the possible themes of user-generated text data; then, the DBSCAN clustering method is applied to classify the behavioural traits of several user groups. Strong scalability and universality as well as accurate identification and classification of user values are shown by experimental validation on two real datasets of the proposed method. Better results in multi-dimensional user values analysis are obtained by the hybrid technique based on topic model and cluster analysis than by the conventional single model approach.