Computational Personality Analysis with Interpretability Empowered Prediction
Paper
Elmahalaw, Ahmed R., Li, Lin, Wu, Xiaohua, Tao, Xiaohui and Yong, Jianming. 2024. "Computational Personality Analysis with Interpretability Empowered Prediction." 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ). Tianjin, China 08 - 10 May 2024 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/CSCWD61410.2024.10580811
Paper/Presentation Title | Computational Personality Analysis with Interpretability Empowered Prediction |
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Presentation Type | Paper |
Authors | Elmahalaw, Ahmed R., Li, Lin, Wu, Xiaohua, Tao, Xiaohui and Yong, Jianming |
Journal or Proceedings Title | Proceedings of the 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024) |
Journal Citation | pp. 413-418 |
Number of Pages | 6 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISBN | 9798350349184 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CSCWD61410.2024.10580811 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10580811 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10579968/proceeding |
Conference/Event | 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ) |
Event Details | 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ) Parent International Conference on Computer Supported Cooperative Work in Design Delivery In person Event Date 08 to end of 10 May 2024 Event Location Tianjin, China |
Abstract | Personality analysis can help individuals gain self-awareness, improve decision-making skills, and enhance relationships, while also providing valuable insights in fields like psychology, human resources, and marketing. Computational models, including traditional machine learning and deep learning models, have been beneficial in analyzing the impact of personality in sociological studies, especially deep learning models with interpretability. However, different computational models may produce different explanation results, which poses a challenge for social studies researchers in selecting appropriate explanations, as each model provides distinct prediction accuarcy values and explanation. To this end, this work introduces a computational personality analysis framework that incorporates Local Interpretable Model-Agnostic Explanations (LIME) to investigate analysis methods. The framework covers various computational personality models, encompasses pre-processing techniques, feature embedding, and focuses specifically on Myers-Briggs Type Indicator (MBTI) personality analysis, enabling valuable insights from predictions generated by diverse computational models. Cosine similarity is employed to evaluate the variations in explanation results produced by different computational models in relation to personality analysis. Our experiment reveals the key finding: although they have comparable predictive accuracy, there are not small explanation differences among the computational models. |
Keywords | Cosine Similarity; Personality Analysis; Interpretability Machine Learning (ML); MBTI; LIME; Cosine Similarity |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460508. Information retrieval and web search |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
School of Mathematics, Physics and Computing | |
School of Business |
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https://research.usq.edu.au/item/z9957/computational-personality-analysis-with-interpretability-empowered-prediction
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