Automatic Personality Research Articles

MSAPersonality: a modern standard Arabic dataset for personality recognition

Automatic personality recognition is a task that attempts to automatically infer personality traits from a variety of data sources, including Text. Our words, whether spoken or written, reveal a lot about who we are. As people speak different languages, each with its own set of characteristics and level of complexity, identifying their personalities automatically might be language-dependent. This task requires an annotated text corpus with personality traits. However, the lack of corpora for languages other than English makes the task extremely challenging. We concentrated our efforts in this paper on the Arabic language in particular because it is understudied and lacks a corpus, despite being one of the most widely spoken languages in the world. Our primary goal was constructing our “MSAPersonality” dataset, which consists of 267 texts in modern standard Arabic that have been annotated with the Big Five personality traits. To evaluate the dataset and its potential for classification and regression, we used text preprocessing techniques, feature extraction, and machine learning algorithms. We obtained promising experimental results. Therefore, further research into predicting personality from Arabic text can be conducted.

Automatic Personality Recognition in Videos using Dynamic Networks and Rank Loss

Automatic Personality Recognition in Videos using Dynamic Networks and Rank Loss

Learning Person-Specific Cognition From Facial Reactions for Automatic Personality Recognition

This paper proposes to recognise the true (self-reported) personality traits from the target subject's cognition simulated from facial reactions. This approach builds on the following two findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions, individuals' nonverbal behaviours are influenced by their conversational partner's behaviours. In this context, we hypothesise that during a dyadic interaction, a target subject's facial reactions are driven by two main factors: their internal (person-specific) cognitive process, and the externalised nonverbal behaviours of their conversational partner. Consequently, we propose to represent the target subject's (defined as the listener) person-specific cognition in the form of a person-specific CNN architecture that has unique architectural parameters and depth, which takes audio-visual non-verbal cues displayed by the conversational partner (defined as the speaker) as input, and is able to reproduce the target subject's facial reactions. Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function, which is then represented as a graph representation for recognising the target subject's true personality. Experimental results not only show that the produced graph representations are well associated with target subjects' personality traits in both human-human and human-machine interaction scenarios, and outperform the existing approaches with significant advantages, but also demonstrate that the proposed novel strategies help in learning more reliable personality representations.

Personality Trait Detection using an Hierarchy of Tree-transformers and Graph Attention Network

Automatic personality trait detection from a person’s writings is helpful for professionals to assess the mental health of an individual, as well as helping individuals to determine their strengths and weaknesses for making choices such as personal improvement, workplace compatibility, and life-style decision-making. Psychologists have identified a set of personality traits that may be present in an individual’s personality. This work classifies the writings of an individual into a subset of these traits. The classifier model comprises an hierarchical structure of tree-transformers and a graph attention network (GAT). The tree-transformers encode the sentences and the following GAT layer encodes the complete text of an individual’s writing. Our model has shown a large performance boost over two benchmark corpora compared to previous works.

Personality Detection using Kernel-based Ensemble Model for Leveraging Social Psychology in Online Networks

The Asian social networking market dominates the world landscape with the highest consumer penetration rate. Businesses and investors often look for winning strategies to attract consumers to increase revenues from sales, advertisements, and other services offered on social media platforms. Social media engagement and online relational cohesion have often been defined within the frameworks of social psychology and personality identification is a possible way in which social psychology can inform, engage, and learn from social media. Personality profiling has many real-world applications, including preference-based recommendation systems, relationship building, and career counseling. This research puts forward a novel kernel-based soft-voting ensemble model for personality detection from natural language, KBSVE-P. The KBSVE-P model is built by first evaluating the performance of various Support Vector Machine (SVM) kernels, namely radial basis function (RBF), linear, sigmoidal, and polynomial, to find the best-suited kernel for automatic personality detection in natural language text. Next, an ensemble of SVM kernels is implemented with a variety of voting techniques, such as soft voting, hard voting, and weighted hard voting. The model is evaluated on the publicly available Kaggle_MBTI dataset and a novel South Asian, Indian, low-resource Hindi language_MBTI (pronounced as vishesh charitr, meaning personality in Hindi) dataset for detecting a user's personality across four personality traits, namely introvert/extrovert (IE), thinking/feeling (TF), sensing/intuitive (SI), and judging/perceiving (JP). The proposed kernel-based ensemble with soft voting, KBSVE-P, outperforms the existing models on English Kaggle-MBTI dataset with an average F-score of 85.677 and achieves an accuracy of 66.89 for the Hindi_MBTI dataset.

Personality Types and Traits—Examining and Leveraging the Relationship between Different Personality Models for Mutual Prediction

The popularity of social media services has led to an increase of personality-relevant data in online spaces. While the majority of people who use these services tend to express their personality through measures offered by the Myers–Briggs Type Indicator (MBTI), another personality model known as the Big Five has been a dominant paradigm in academic works that deal with personality research. In this paper, we seek to bridge the gap between the MBTI, Big Five and another personality model known as the Enneagram of Personality, with the goal of increasing the amount of resources for the Big Five model. We further explore the relationship that was previously reported between the MBTI types and certain Big Five traits as well as test for the presence of a similar relationship between Enneagram and Big Five measures. We propose a new method relying on psycholingusitc features selected based on their relationship with the MBTI model. This approach showed the best performance through our experiments and led to an increase of up to 3% in automatic personality recognition for Big Five traits on the per-trait level. Our detailed experimentation offers further insight into the nature of personality and into how well it translates between different personality models.

A Media-Guided Attentive Graphical Network for Personality Recognition Using Physiology

Physiological automatic personality recognition has been largely developed to model an individuals personality trait from a variety of signals. However, few studies have tackled the problems of integration methodology from multiple observations into a single personality prediction. In this study, we focus on finding a novel learning architecture to model the personality trait under a Many-to-One scenario. We propose to integrate not only the information on the user but also consider the effect of the affective multimedia stimulus. Specifically, we present a novel Acoustic-Visual Guided Attentive Graph Convolutional Network for enhanced personality recognition. The emotional multimedia content guides the formation of the physiological responses into a graph-like structure to integrate latent inter-correlation among all responses toward affective multimedia. Then these graphs would be further processed by the Graph Convolutional Network (GCN) to jointly model instances and inter-correlation levels of the subjects responses. We show that our model outperforms the current state of the art on two large public corpora for personality recognition. Further analysis reveals that there indeed exists a multimedia preference for inferring personality from physiology, and several frequency-domain descriptors in ECG and the tonic component in EDA are shown to be robust for automatic personality recognition.

Vision and Audio-based Methods for First Impression Recognition Using Machine Learning Algorithms: A Review

Personality is a psychological construct that embodies the unique characteristics of an individual. Automatic personality computing enables the assessment of personality elements with the help of machines. Over the last few decades, a lot of researchers have focussed on computing aspects of personality, emotions, and behavior with the help of machine learning. Efficient personality recognition using machine learning can provide inroads to almost every field of human advancement. However, we found out there are not enough good surveys to consolidate the progress of the growing field. In our effort, we have taken a sub-field of personality computation known as apparent personality perception or First Impressions. In addition to providing an exhaustive compilation of available First Impression research, we have classified the existing literature according to the data modalities and machine learning techniques they have utilized. Our work also lists various limitations and gaps within the existing literary works with possible measures to address them. The paper concludes with our comments on the future work in the field of first impressions.

A lexical-availability-based framework from short communications for automatic personality identification

We store and retrieve words from our brain during a communicative intention. These words form what linguistics and psycholinguistics refer to as the mental lexicon —a cognitive construct—. In this paper, we study the effectiveness of such a cognitive-based model for selecting the relevant lexicon that an automatic classifier can leverage for learning to distinguish personality traits from short communication intentions. Our proposed approach can automatically detect lexical units that are more suitable to train a machine learning algorithm to identify a subject’s personality trait. We evaluated our method in two Mexican Spanish datasets, labeled according to the Big Five personality model. Experimental results indicate some personality traits are more transparent than others, e.g., Extroversion and Openness with 71% and 72% F-scores, respectively. An analysis of the information our proposed method selects identifies relevant psycholinguistic cues that complement psychologists’ a prior knowledge. Overall, contrary to deep NNs based models, our proposed approach represents a less expensive and more interpretable technique, the desired combination for systems that aim to support the decisions made by a specialist.

Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals’ personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.

Identifying Big Five personality traits based on facial behavior analysis.

The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37–0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75–0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis.

Text-based automatic personality prediction: a bibliographic review

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception.

In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.

Automatic Personality Recognition Used In Asynchronous Video Interviews

With the use of artificial intelligence (AI), the automatic recognition of individual has become an active area of research. And it has a wide application in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolution neural network (CNN) models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this study, an end-to-end Artificial Intelligence interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the Automatic video Interviews and the true personality scores from the facial expressions and self-reported questionnaires of real job applicants.

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction.

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

Automatic personality prediction: an enhanced method using ensemble modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

Automatic Personality Assessment through Movement Analysis.

Obtaining accurate and objective assessments of an individual’s personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee’s personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual’s personality through his or her movements and open up pathways for several research.

Deep Personality Trait Recognition: A Survey.

Automatic personality trait recognition has attracted increasing interest in psychology, neuropsychology, and computer science, etc. Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality trait recognition. This paper systematically presents a comprehensive survey on existing personality trait recognition methods from a computational perspective. Initially, we provide available personality trait data sets in the literature. Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Next, we describe the details of state-of-the-art personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods are analyzed and summarized in both single modality and multiple modalities, such as audio, visual, text, and physiological signals. Finally, we analyze the challenges and opportunities in this field and point out its future directions.

Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?

Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like software engineering. In this article, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers’ e-mails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in software engineering by replacing the personality detection tool used to infer developers’ personalities from pull-request discussions and e-mails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain.

Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings

The study of automatic personality recognition has gained attention in the last decade thanks to a variety of applications that derive from this field. The big five model (also known as OCEAN) constitutes a well-known method to label different personality traits. This work considers transliterations of video recordings collected from YouTube (originally provided by the Idiap research institute) and automatically generated scores for the five personality traits which also were provided in the database. The transliterations are modeled with two different word embedding approaches, Word2Vec and GloVe and three different levels of analysis are included: regression to predict the score of each personality trait, binary classification between strong vs. weak presence of each trait, and the tri-class classification according to three different levels of manifestations in each trait (low, medium, and high). According to our findings, the proposed approach provides similar results to others reported in the state-of-the-art. We think that further research is required to find better results. Our results, as well as others reported in the literature, suggest that there is a big gap in the study of personality traits based on linguistic patterns, which make it necessary to work on collecting and labeling data considering the knowledge of expert psychologists and psycholinguists.