Personal Affective Trait Computing Using Multiple Data Sources

The personal affective trait (PAT) is a relatively stable affective trait that reflects differences among individuals. PAT is crucial to better understanding individuals in various applications, such as human-computer interaction (HCI) and personalized services. PAT computing can calculate several individual affective traits based on personal data. Because the PAT contributes to personality, PAT computing is based predominantly on personality computing. Moreover, the PAT can be classified into labeled and unlabeled PAT according to whether corresponding results from a psychological questionnaire. A general PAT computing process is proposed in order to compute various PAT categories. Because PAT is expressed in different forms under different scenarios, such as speech, gesture and context, PAT computing uses multiple data sources to generate comprehensive and accurate results. In order to illustrate the feasibility of the proposed PAT computing, 13 participants are recruited and three experiments completed so that their personal data can be collected and analyzed. Labeled PAT computing measures the PAT by fitting, while unlabeled PAT computing uses clustering. Furthermore, two PAT types - affective intensity and emotional stability - are computed using the analyzed data features. In order to verify the calculated PAT, the correlation between PAT and personality is measured.