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Lingual markers for automating personality profiling: background and road ahead

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Abstract

Personality is a psychological concept which embodies the unique characteristics of an individual. An individual’s distinct traits are embodied by the psychological concept of personality. The Lexical Hypothesis states that language use and the terms people use to describe one another can help us decide personality qualities. Huge improvements in data collecting and processing have been brought about by technological breakthroughs. These could help to develop autonomous personality assessment models by deriving linguistic markers from the data present in social media, telecommunication signals, and even signals collected from human–machine interaction. Numerous studies have cantered on using machine learning to automate personality recognition from text. However, there are questions in terms of their performance, reliability as well as ethical usage. To find solutions, we extensively review and analyse the existing research in the field of personality computing using lingual markers in text. A content-oriented classification of the techniques used is provided. We also examine the existing literature for gaps and limitations with a detailed comparative analysis. The field of personality computing has the potential to impact every field of human life but the progress as of now is limited. Our review will help researchers to build from what has been achieved so far for faster progress in the field.

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Data availability

Essays: [James W. Pennebaker, Laura A. King]. ([Year & Month of dataset creation]). [Essays], [Version 1]. Retrieved [18-08-2022] from [essays.csv | Kaggle]. (https://www.kaggle.com/datasets/manjarinandimajumdar/essayscsv) MyPersonality: The dataset was collected from Facebook by David Stillwell and Michal Kosinki for the myPersonality project. [David Stillwell, Michal Kosinski]. ([2012; Month of dataset creation]). [MyPersonality], [Version of the dataset]. Retrieved [18-08-2022] from [wiki:mypersonality_final]. Citation for the MyPersonality Project. Kosinski, M., Matz, S., Gosling, S., Popov, V. & Stillwell, D. (2015) Facebook as a Social Science Research Tool: Opportunities, Challenges, Ethical Considerations and Practical Guidelines. American Psychologist.(https://web.archive.org/web/20180428085315/http://mypersonality.org/wiki/lib/exe/fetch.php?media=wiki:mypersonality_final.zip) PAN-AP-15: [Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Potthast, Benno Stein, and Walter Daelemans] ([2015; September]). [PAN15-Author-Profiling], [Version 1]. Retrieved [Date Retrieved] from [PAN Data (webis.de)]. (https://pan.webis.de/data.html#pan15-author-profiling) YouTube: [Joan-Isaac Biel, Daniel Gatica-Perez] ([2012]). [Youtube Personality], [Version 1]. The dataset was originally released by Idiap institute, no longer available for download on updated website [Youtube Personality—English (idiap.ch)] Retrieved [18-08-2022]. However, we were able to download it from OpenM [Youtube] Retrieved [18-08-2022]. (https://www.openml.org/search?type=data&sort=runs&id=41411&status=active)

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  1. Department of Computer Science, University of Kashmir Hazratbal, Hazratbal, Srinagar, 190006, India

    Mohmad Azhar Teli & Manzoor Ahmad Chachoo

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Limitations of the survey

We tried our best to include all the works in the domain of Automatic Personality Recognition from text, which makes comparing the computational techniques extremely difficult. Further, non-inclusion of trait theories other than the Big 5 model might have led to missing computational techniques that have better performance.

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Appendix

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Table 5 Performance Comparison of APRT (Essays)
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Table 6 Performance Comparison of APRT (Facebook)
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6,

Table 7 Performance comparison of APRT (YouTube)
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Table 8 Performance comparison of APRT (Twitter)
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Teli, M.A., Chachoo, M.A. Lingual markers for automating personality profiling: background and road ahead. J Comput Soc Sc 5, 1663–1707 (2022). https://doi.org/10.1007/s42001-022-00184-6

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