Recent trends in deep learning based personality detection

Mehta, Yash; Majumder, Navonil; Gelbukh, Alexander; Wang, Zhaoxia

doi:10.1007/s10462-019-09770-z

Cited by 247 publications

(140 citation statements)

References 94 publications

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“…In general, researchers in computer science and human–computer interaction were quicker than those in psychology to apply ML methods to the task of personality prediction. In fact, automated personality detection has emerged as its own separate field (Majumder et al, 2017; Mehta et al, 2020), in which the psychological literature is only cited with respect to the personality inventories that are used as target variables. As noted by Mønsted et al (2018), some work coming out of this new field suffers from small, unrepresentative samples and questionable modelling practices (e.g.…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

et al. 2020

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The increasing availability of high‐dimensional, fine‐grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

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Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

et al. 2020

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“…On the other hand, studies have been performed where audio and video data are used by DL-based models to predict personality [21]. One study, performed by Levitan et al (2016), focused on the automatic identification of traits such as gender, deception and personality using acoustic-prosodic and lexical features [22].…”

Section: Machine Learning For Personality Assessmentmentioning

confidence: 99%

An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning

et al. 2020

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Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.

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“…In general, researchers in computer science and human-computer interaction were quicker than those in psychology to apply ML methods to the task of personality prediction. In fact, automated personality detection has emerged as its own separate field (Majumder, Poria, Gelbukh, & Cambria, 2017;Mehta, Majumder, Gelbukh, & Cambria, 2019), in which the psychological literature is only cited with respect to the personality inventories that are used as target variables. As noted by Mønsted et al (2018), some work coming out of this new field suffers from small, unrepresentative samples and questionable modeling practices (e.g., model overfitting; Chittaranjan et al, 2013;De Montjoye et al, 2013;W.…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

“…Similar toMehta et al (2019), the knowledge tracing literature serves as an example of a task traditionally located in educational psychology which is now increasingly solved with state-of-the-art ML technology(Piech et al, 2015).…”

mentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

Stachl¹,

Pargent²,

Hilbert³

et al. 2019

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The increasing availability of high-dimensional, fine-grained data about human behavior, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyze the data and interpret the results appropriately. Machine learning models are well-suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modeling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modeling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

show abstract

Recent trends in deep learning based personality detection

Cited by 247 publications

References 94 publications

Personality Research and Assessment in the Era of Machine Learning

Personality Research and Assessment in the Era of Machine Learning

An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning

Personality Research and Assessment in the Era of Machine Learning

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