Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

Personality Classification Of Social Media Users Based On Type Of Work And Interest In Information

Social media is a platform that makes it easier for users to interact and get to know each other because in social media there are profiles, statuses, and user uploads. Therefore, many studies utilize social media because there is much information that can be explored on social media, one of which is research on the personality classification of social media users. However, many studies related to personality classification of social media users have failed due to too many model target classes, which result in low accuracy. In this research, the author uses the Myers-Briggs Type Indicator (MBTI) model, which is focused on only two personality classes, namely "Introvert/Extrovert" and "Sensor/Intuitive" with the features type of work and interest in information which are feature representations of the personality class used to reduce the target class. The best accuracy result is 95.87% after classifying using two personality classes

Effects of grandiose and vulnerable narcissism on donation intentions:The moderating role of donation information openness

This study investigated the relationship between two subtypes of narcissism (grandiose vs. vulnerable) and donation intentions, while considering the moderating effects of donation information openness. The results of an experimental survey of 359 undergraduate students showed that individuals who scored high on grandiose narcissism showed greater donation intentions when the donor’s behavior was public, while they showed lower donation intentions when it was not. In addition, individuals who scored high on vulnerable narcissism showed lower donation intentions when the donor’s behavior was not public. This study contributes to narcissism and the donation behavior literature and proposes theoretical and practical implications as per narcissistic individual differences. Future research possibilities are also discussed

HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding

Personality detection along with other behavioural and cognitive assessment can essentially explain why people act the way they do and can be useful to various online applications such as recommender systems, job screening, matchmaking, and counselling. Additionally, psychometric NLP relying on textual cues and distinctive markers in writing style within conversational utterances reveal signs of individual personalities. This work demonstrates a text-based deep neural model, HindiPersonalityNet of classifying conversations into three personality categories {ambivert, extrovert, introvert} for detecting personality in Hindi conversational data. The model utilizes GRU with BioWordVec embeddings for text classification and is trained/tested on a novel dataset, शख्सियत (pronounced as Shakhsiyat) curated using dialogues from an Indian crime-thriller drama series, Aarya. The model achieves an F1-score of 0.701 and shows the potential for leveraging conversational data from various sources to understand and predict a person's personality traits. It exhibits the ability to capture semantic as well as long-distance dependencies in conversations and establishes the effectiveness of our dataset as a benchmark for personality detection in Hindi dialogue data. Further, a comprehensive comparison of various static and dynamic word embedding is done on our standardized dataset to ascertain the most suitable embedding method for personality detection

Big five personality prediction based in Indonesian tweets using machine learning methods

The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features

Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

Performance Analysis of State-of-the-Art Deep Learning Models in the Visual-Based Apparent Personality Detection

This paper analyses the performances of pre-trained deep learning models as feature extractors for apparent personality trait detection (APD) by utilising different statistical methods to find the best performing pre-trained model. Accuracy and computational cost were used to measure the model performance. Personality is measured using the Big Five Personality Schema. CNN-RNN networks were designed using VGG19, ResNet152, and VGGFace pre-trained models to measure the personality with scene data. The models were compared using the mean accuracy attained and the average time is taken for training and testing. Descriptive statistics, graphs, and inferential statistics were applied in model comparisons. Results convey that, ResNet152 model reported the highest mean accuracy in the test dataset (0.9077), followed by VGG19 with 0.9036; VGGFace recorded the lowest (0.8962). ResNet152 consumed more time than other architectures in model training and testing since the number of parameters is comparably higher than the other two architectures involved. Statistical test results prove no significant evidence to conclude that VGG19 and ResNet152 based CNN-RNN models performed differently. This leads to the conclusion that even with a comparably lower number of parameters VGG19 model performed well. The findings reveal that satisfactory accuracy is obtained with a limited number of frames extracted from videos since models achieved more than 90% accuracy

Authorial approach to the detection of selected psychological traits based on handwritten texts

The study sought to use computer techniques to detect selected psychological traits based on the nature of the writing and to evaluate the effectiveness of the resulting software. Digital image processing and deep neural networks were used. The work is complex and multidimensional in nature, and the authors wanted to demonstrate the feasibility of such a topic using image processing techniques and neural networks and machine learning. The main studies that allowed the attribution of psychological traits were based on two models known from the literature, KAMR and DA. The evaluation algorithms that were implemented allowed the evaluation of the subjects and the assignment of psychological traits to them. The DA model turned out to be more effective than the KAMR model

Effects of Personality on Social Performance in Social Trading

On social trading platforms, the income of leader traders is largely dictated by the number of copy trades conducted by their followers. Consequently, it is imperative for leader traders to exhibit appealing personalities to entice their followers to conduct copy trades. Drawing on social capital theory, we endeavor to scrutinize the effects of traders’ personalities on the accumulation of social capital, which in turn bolsters social performance as measured by the number of copy trades. Data was extracted from a leading social trading platform. The Myers–Briggs Type Indicator personality classification system was then employed to depict leader traders’ personalities based on a novel text-based, machine learning approach. Preliminary analytical results reveal significant relationships among personality traits, social capital dimensions, and social performance. Findings from this study generate insights for social trading platforms and leader traders on exhibiting desirable personalities conducive for accumulating social capital that entice followers to conduct copy trades