Automatic personality assessment: A systematic review

Kedar, Seema; Bormane, D. S.

doi:10.1109/infop.2015.7489402

Cited by 20 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, we applied this procedure to all relevant references reported in prior metaanalyses and reviews (specifically, Azucar et al, 2018;Connelly & Ones, 2010;Kedar & Bormane, 2016;Marengo & Montag, 2020;Settanni et al, 2018;Tskhay & Rule, 2014;Vinciarelli & Mohammadi, 2014). In total, this procedure yielded 178 articles and 201 studies (included within those articles) that met our inclusion criteria (see Supplemental File 2 for the reference list of all articles included in the review).…”

Section: Search Proceduresmentioning

confidence: 99%

Assessing the quality of studies using machine learning for personality assessment: A systematic review

Hinds¹,

Parkhouse²,

Hotchin³

2021

Preprint

View full text Add to dashboard Cite

In recent years, the use of machine learning to predict personality from digital data has gained increasing interest from organisations, academics and the public. In turn, a new field of personality computing has developed, which involves combining machine learning techniques with psychological measures of personality. However, effectively integrating these approaches is challenging - the fields of machine learning and psychology are highly disparate, with different objectives, methodologies, and perspectives on performing and reporting research. In this article, we report findings from a systematic review that analysed 178 personality computing studies published before November 2020. We developed a novel set of criteria that was used to evaluate the quality of study design and reporting of each study according to 10 criteria: hypotheses, study rationale, selection of features, algorithm training, ground truth, sampling, the evaluation of algorithms’ performance (i.e., classification, regression), the performance measures reported, and detail concerning ethics and open science practices. Our findings highlight that a large proportion of studies lack detail on the above criteria, which leads to questions over the validity, reliability, and replicability of the findings. We discuss the implications of this research for practice and recommend directions for future work.

show abstract

Section: Search Proceduresmentioning

confidence: 99%

Assessing the quality of studies using machine learning for personality assessment: A systematic review

Hinds¹,

Parkhouse²,

Hotchin³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Throughout this article, we use the term Personality Computing , coined by Vinciarelli and Mohammadi (2014a), to describe ML approaches to personality research and assessment. However, other terms such as computational personality traits assessment (e.g., Ilmini & Fernando, 2017), digital phenotyping (e.g., Onnela & Rauch, 2016), psychoinformatics (e.g., Markowetz et al., 2014), personality sensing (e.g., Harari et al., 2020a, 2020b), automatic personality detection (e.g., Mehta et al., 2019), ML personality assessment (e.g., Bleidorn & Hopwood, 2019), and automatic personality assessment (e.g., Kedar & Bormane, 2015) have been used to describe similar approaches.…”

Section: Introductionmentioning

confidence: 99%

Personality computing: New frontiers in personality assessment

Phan

Rauthmann

2021

Social & Personality Psych

View full text Add to dashboard Cite

Personality Computing (PC) is a burgeoning field at the intersection of personality and computer science that seeks to extract personality‐relevant information (e.g., on Big Five trait levels) from sensor‐assessed information (e.g., written texts, digital footprints, smartphone usage, non‐verbal behavior, speech patterns, game‐play, etc.). Such sensor‐based personality assessment promises novel and often technologically sophisticated ways to unobtrusively measure individual differences in a highly precise, granular, and faking‐resistant manner. We review the different conceptual underpinnings of PC; survey how well different types of sensors can capture different types of personality‐relevant information; discuss the evaluation of PC performance and psychometric issues (reliability and validity) of sensor‐derived scores as well as ethical, legal, and societal implications; and highlight how modern personality and computer science can be married more effectively to provide practically useful personality assessment. Together, this review aims to introduce readers to the opportunities, challenges, pitfalls, and implications of PC.

show abstract

“…However, these methods are limited to few behaviour types. There are methods proposed for personality assessment [7, 8], which assess person interest, attitude, relationship with family, community, etc. These methods study typed texts by users but not handwritten texts.…”

Section: Introductionmentioning

confidence: 99%

“…Also, some of the rules are obtained based on the experience and psychology of the people, as discussed in the link (https://ipip.ori.org/). It is evident from the literature [7, 8], where we can see several methods are published for studying human behaviour using graphology. For example, when the person is under pressure and in poor condition, it is expected such behaviour reflects in writing.…”

Section: Introductionmentioning

confidence: 99%

Graphology based handwritten character analysis for human behaviour identification

Ghosh

Shivakumara

Roy

et al. 2020

CAAI trans. intell. technol.

View full text Add to dashboard Cite

Graphology‐based handwriting analysis to identify human behavior, irrespective of applications, is interesting. Unlike existing methods that use characters, words and sentences for behavioural analysis with human intervention, we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours. The proposed method extracts structural features, such as loops, slants, cursive, straight lines, stroke thickness, contour shapes, aspect ratio and other geometrical properties, from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules. The derived hypothesis has the ability to categorise the personal, positive, and negative social aspects of an individual. To evaluate the proposed method, an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups. This automatic privacy projected system is available on the website (http://subha.pythonanywhere.com). For quantitative evaluation of the proposed method, several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options. The automatic system receives 5300 responses from the users, for which, the proposed method achieves 86.70% accuracy.

show abstract

Automatic personality assessment: A systematic review

Cited by 20 publications

References 29 publications

Assessing the quality of studies using machine learning for personality assessment: A systematic review

Assessing the quality of studies using machine learning for personality assessment: A systematic review

Personality computing: New frontiers in personality assessment

Graphology based handwritten character analysis for human behaviour identification

Contact Info

Product

Resources

About