AI for personality prediction

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,

The use of artificial intelligence (AI) into personality prediction is a rapidly growing area with ramifications for several sectors. AI systems strive to assess and forecast individual personality traits by utilizing data from many sources, such as social media, online interactions, and questionnaires. The procedure starts with meticulous data collecting to acquire information on an individual’s behavior, preferences, and activities.

  • Feature Extraction and Machine Learning Models

AI-driven personality prediction involves the extraction of relevant features from the collected data. Natural language processing (NLP) techniques are applied for textual data, while computer vision algorithms handle visual content. Machine learning models, ranging from traditional approaches like regression and decision trees to more sophisticated neural networks, are then employed to predict personality traits [1-3]. These models learn patterns and relationships from labeled datasets, where personality traits are already known.

  • Psycholinguistic Analysis and Social Media Insights

An essential aspect of AI-driven personality prediction is psycholinguistic analysis, wherein linguistic patterns associated with specific personality traits are identified. This can include linguistic styles, emotional tones, and word choices [4]. Social media platforms serve as rich sources of data, and AI algorithms analyze posts, likes, comments, and interactions to infer personality traits. Insights gained from online behavior contribute to a more nuanced personality profile [5].

  • Ethical Considerations and Applications

The use of AI for personality prediction raises ethical considerations, particularly regarding privacy and consent. Ensuring transparency in data collection practices and obtaining informed consent are crucial aspects of ethical implementation [6]. The applications of personality prediction are diverse, ranging from recruitment and marketing to mental health assessments. Employers may use personality assessments in hiring processes, businesses can tailor marketing strategies based on predicted consumer personalities, and AI may assist in identifying potential psychological issues through behavior analysis.

  • Limitations and Future Directions:

Despite the promises, AI-driven personality prediction has its limitations. Predictions may not always be accurate due to the complexity of personality, individual differences, and cultural variations [7]. Ensuring the interpretability of AI models is essential for ethical use, allowing users to understand how and why a prediction is made [8]. As technology advances and our understanding of human behavior deepens, the field of AI for personality prediction is poised to evolve, offering both opportunities and challenges in the realms of privacy, ethics, and societal impact.


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  2. Mehta, Y., Majumder, N., Gelbukh, A., & Cambria, E. (2020). Recent trends in deep learning based personality detection. Artificial Intelligence Review, 53, 2313-2339.
  3. Jang, J., Yoon, S., Son, G., Kang, M., Choeh, J. Y., & Choi, K. H. (2022). Predicting personality and psychological distress using natural language processing: a study protocol. Frontiers in Psychology, 13, 865541.
  4. Sterling, J., Jost, J. T., & Bonneau, R. (2020). Political psycholinguistics: A comprehensive analysis of the language habits of liberal and conservative social media users. Journal of personality and social psychology, 118(4), 805.
  5. Trifan, A., Antunes, R., Matos, S., & Oliveira, J. L. (2020, April). Understanding depression from psycholinguistic patterns in social media texts. In European Conference on Information Retrieval (pp. 402-409). Cham: Springer International Publishing.
  6. Völkel, S. T., Haeuslschmid, R., Werner, A., Hussmann, H., & Butz, A. (2020, April). How to Trick AI: Users’ strategies for protecting themselves from automatic personality assessment. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-15).
  7. Alexander III, L., Mulfinger, E., & Oswald, F. L. (2020). Using big data and machine learning in personality measurement: Opportunities and challenges. European Journal of Personality, 34(5), 632-648.
  8. Ramon, Y., Farrokhnia, R. A., Matz, S. C., & Martens, D. (2021). Explainable AI for psychological profiling from behavioral data: An application to big five personality predictions from financial transaction records. Information, 12(12), 518.
  9. Wang, L., Li, L., Li, J., Li, J., Gupta, B. B., & Liu, X. (2018). Compressive sensing of medical images with confidentially homomorphic aggregations. IEEE Internet of Things Journal6(2), 1402-1409.
  10. Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021). InFeMo: flexible big data management through a federated cloud system. ACM Transactions on Internet Technology (TOIT)22(2), 1-22.
  11. Gupta, B. B., Perez, G. M., Agrawal, D. P., & Gupta, D. (2020). Handbook of computer networks and cyber security. Springer10, 978-3.
  12. Bhushan, K., & Gupta, B. B. (2017). Security challenges in cloud computing: state-of-art. International Journal of Big Data Intelligence4(2), 81-107.

Cite As:

Vajrobol V. (2024) AI for personality prediction, Insights2Techinfo, pp.1

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