Abstract:

Aiming at the problem of the personality prediction of social network users, we proposed a method that combined information gain and semantic features to refine user’s text information, and adopted a method of multilabel classification algorithm for comprehensive prediction. Firstly, lexical features in text were extracted based on information gain, including sentiment word, part of speech and tense etc, and feature selection and weighting were carried out. For semantic features, text content was mapped to ontology concepts and then semantic relevance was calculated. Secondly, based on the combined influence of lexical features and semantic features, a multilabel classification algorithm was used to execute personality prediction process. Text information was
 handled from different perspectives and label relevance was taken into full consideration. Experimental results verify the effectiveness of the proposed method.

Key words: social network, personality prediction, social computing, multilabel classification