Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data
| Autor: | Nicholas D. Lane, Matthieu Vegreville, Gabriella M. Harari, Angela Chieh, Catherine Tong, Otmane Bellahsen, Eva Roitmann |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 010401 analytical chemistry 05 social sciences Decision tree Inference Machine learning computer.software_genre Personality psychology 01 natural sciences 050105 experimental psychology 0104 chemical sciences Support vector machine Binary classification Personality 0501 psychology and cognitive sciences Artificial intelligence Big Five personality traits Scale (map) business computer media_common |
| Zdroj: | MobiSys |
| DOI: | 10.1145/3210240.3210823 |
| Popis: | We present the first large-scale (9270-user) study of data from both mobile and networked appliances for Big-Five personality inference. We correlate aggregated behavioral and physical health features with personalities, and perform binary classification using SVM and Decision Tree. We find that it is possible to infer each Big-Five personality at accuracies of 75% from this dataset despite its size and complexity (mix of mobile and appliance) as prior methods offer similar accuracy levels. We would like to achieve better accuracies and this study is a first step towards seeing how to model such data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|