Big Five Trails: A Critical Review

Najm, Najm Abood

doi:10.22146/gamaijb.34931

Cited by 18 publications

(10 citation statements)

References 73 publications

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“…Author details 1 Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia. 2 Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.…”

Section: Authors' Informationmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

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The ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can give valuable insights on their personalities (e.g., in terms of predicting job satisfaction, specific preferences, as well as the success of professional and romantic relationship) and getting it without the hassle of taking formal personality test. Termed personality prediction, the process involves extracting the digital content into features and mapping it according to a personality model. Owing to its simplicity and proven capability, a well-known personality model, called the big five personality traits, has often been adopted in the literature as the de facto standard for personality assessment. To date, there are many algorithms that can be used to extract embedded contextualized word from textual data for personality prediction system; some of them are based on ensembled model and deep learning. Although useful, existing algorithms such as RNN and LSTM suffers from the following limitations. Firstly, these algorithms take a long time to train the model owing to its sequential inputs. Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. To address these aforementioned limitations, this paper introduces a new prediction using multi model deep learning architecture combined with multiple pre-trained language model such as BERT, RoBERTa, and XLNet as features extraction method on social media data sources. Finally, the system takes the decision based on model averaging to make prediction. Unlike earlier work which adopts a single social media data with open and close vocabulary extraction method, the proposed work uses multiple social media data sources namely Facebook and Twitter and produce a predictive model for each trait using bidirectional context feature combine with extraction method. Our experience with the proposed work has been encouraging as it has outperformed similar existing works in the literature. More precisely, our results achieve a maximum accuracy of 86.2% and 0.912 f1 measure score on the Facebook dataset; 88.5% accuracy and 0.882 f1 measure score on the Twitter dataset.

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Section: Authors' Informationmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

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“…Trait merupakan dorongan dalam diri yang memengaruhi perilaku. Trait mewakili kepribadian yang ditunjukkan dalam pola respon berulang terhadap suatu hal, peristiwa, atau keadaan (Abood, 2019). Trait yang menjadi dorongan remaja dalam berperilaku akan mengarahkan remaja untuk mencari bantuan ketika menghadapi masalah yang memengaruhi kondisi psikologisnya.…”

Section: Pendahuluanunclassified

Mental Health Literacy Ditinjau Dari Big Five Personality Traits Pada Remaja Di Yogyakarta

Yuliasari,

Pusvitasari

2023

JPSP

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Remaja merupakan generasi berkelanjutan yang dapat membangun bangsa dimana remaja yang sehat secara fisik dan mental dengan memiliki pengetahuan kesehatan mental akan mampu menghadapi perubahan zaman. Namun, pada kenyataannya dalam hal literasi kesehatan mental banyak diantaranya yang masih belum mendapatkan pengetahuan itu. Literasi kesehatan mental dapat ditinjau dari kepribadian seseorang. Kepribadian dapat diukur dengan Big Five personality traits, yang terdiri dari extraversion, agreeableness, conscientiousness, negative emotionality dan open-mindedness. Penelitian ini bertujuan untuk melihat pengaruh Big Five personality traits terhadap mental health literacy pada remaja di Yogyakarta. Subjek penelitian terdiri dari 200 siswa berusia 13–18 tahun dan berdomisili di Yogyakarta. Alat ukur yang digunakan adalah Mental Health Literacy Scale (MHLS) dan Big Five Inventory. Analisis data menggunakan uji analisis regresi berganda dan uji korelasional antarvariabel. Hasil extraversion trait memiliki korelasi positif yang signifikan dengan literasi kesehatan mental (r = .01, p < .05), sedangkan negative emotionality trait memiliki korelasi negatif yang signifikan dengan literasi kesehatan mental (r = - .015, p < .05). Hasil penelitian menunjukkan bahwa Big Five personality traits menjadi prediktor literasi kesehatan mental. Extraversion trait (β = .219, p < .05) merupakan prediktor yang lebih kuat dibandingkan dengan trait kepribadian lainnya.

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“…Due to the specificity of test answers, it might be difficult to draw broad conclusions from them. This makes implementing the knowledge difficult (Abood, 2019. ) Cross-cultural personality studies include definitional and measurement issues.…”

Section: Literature Reviewmentioning

confidence: 99%

A Critical Analysis of Big Five Personality Theory

Naeem

2023

IJARI

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Through a lengthy history of conceptual and applied research in a variety of fields, this study aims to give a comprehensive and in-depth overview of the literature on the big five characteristics (BFT). These studies were conducted using large samples of people, groups, and nations. Because the BFT is the model for describing the structure of personality traits that is the most widely accepted, the purpose of this study was to identify them in order to achieve a better understanding of them and so that researchers can take them into consideration when conducting their future studies. The eight most important observations pertaining to the BFT were the primary subject of this review. The significance of the traits, the background of the BFT model, the unity or hierarchy of the traits, the quantity of the traits, the three viewpoints on BFT, the BFT surveys, the relationship between BFT and performance, and the requirement for a sixth feature are these findings. The findings of the study unequivocally shown that there are flaws in universal models, which have a propensity to generalize a constrained number of characteristics in order to provide an interpretation of personality that is applicable across national boundaries and cultural contexts.

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Big Five Trails: A Critical Review

Cited by 18 publications

References 73 publications

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Mental Health Literacy Ditinjau Dari Big Five Personality Traits Pada Remaja Di Yogyakarta

A Critical Analysis of Big Five Personality Theory

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