Feature Analysis and Optimisation for Computational Personality Recognition
| Autor: | Chunhua Wu, Mao Yu, Dongmei Zhang, Xiujuan Wang, Kangfeng Zheng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Social network
business.industry Computer science media_common.quotation_subject Feature extraction Particle swarm optimization 02 engineering and technology Machine learning computer.software_genre Statistical classification 020204 information systems 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Personality 020201 artificial intelligence & image processing Artificial intelligence Big Five personality traits business computer media_common |
| Zdroj: | 2018 IEEE 4th International Conference on Computer and Communications (ICCC). |
| Popis: | Automatically classifying human personality traits through analysis of their social network behaviors is an important yet challenging task to date considering the low accuracy of current researches. In that detection of significant features is an essential part of a personality recognition system, this paper proposes an in-depth analysis of features that contributes to the recognition of a given trait. Besides the common features of social network used by most current researches, text style features and TF-IDF-based psychological features are proposed and prove to be effective to predict certain personality trait. Also particle swarm optimization (PSO) feature optimization algorithm has been adopted to select the best combination of features. Simulation results show that with the best combination of features, the F-measure value of the personality recognition has been improved around 12%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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