Big-Five Personality Traits Based on Four Main Methods

Hima, P.; Shanmugam, M.

doi:10.1007/978-981-13-9187-3_65

Cited by 3 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), exposing their thoughts, feelings and opinions, they allow analyzers to scrutinize the users' different activities, such as likes, visits, mentions, forwards, replies, friends chain, and many others depending on the context. Hima and Shanmugam investigated the correlation between different users' behaviors in social media and the Big Five personality traits [13]; namely, they found that extroversion is correlated with the number of friends or followers, more social media groups, frequent uses of social media, and neuroticism is correlated with less use of private messages, sharing more information, spending more time in social media and so on. Chen et al [14] called into question that, is it possible for enterprises to obtain the personality information of customers unconsciously, to employ an effective communication strategy?…”

Section: Social Media Activitiesmentioning

confidence: 99%

“…Recent trends in APP have led to a proliferation of studies through analyzing different data types, in particular analyzing speech [4][5][6], image [7][8][9], video [10][11][12], , social media contents and activities [13][14][15], emails [16], handwriting [17,18], touch screen-based interaction [19], signature [20], nonverbal behaviors [21], mobile short message services (SMS) [22], and so on. Along with this growth in APP, however there is an increasing concern over the accuracy of the predictions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Ramezani,

Feizi-Derakhshi,

Balafar

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Social Media Activitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Ramezani,

Feizi-Derakhshi,

Balafar

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…(1) We proposed a framework to determine Big Five personality traits through handwriting images using machine learning classifcation. (2) From the experiments, it can be seen that the proposed framework is very efective and performs the state-of-the-art classifcation methods for determining the Big Five personality traits through handwriting images.…”

Section: Introductionmentioning

confidence: 95%

“…Te Five Factor Model (FFM) of personality is a set of fve broad personality trait dimensions, often referred to as the "Big Five Model," which consist of openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism [2]. Te application of the Big Five model has been consistently associated with career guidance and job performance [3], analysing fnancial behaviour [4], employee recruitment [5], and marital relations [6].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology

Samsuryadi

Kurniawan

Supardi

et al. 2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.

show abstract