Architecture for Personality Detection Using Enneagram Knowledge: Case Study

Abdelhamid, Esraa; Ismail, Sally; Aref, Mostafa

doi:10.21608/ijicis.2023.137912.1181

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…The Myers-Briggs Type Indicator categorizes people into four categories: intuitive (N), sensing (S), thinking (T) and feeling (F), with each category containing four similar traits (Amirhosseini & Kazemian, 2020). The Enneagram model categorizes people as nine different types of personalities, with each type having a single defining trait (Abdelhamid et al, 2023). Each personality classification has its own strengths and weaknesses; for example, those who are extroverts may have an easier time making.…”

Section: Related Workmentioning

confidence: 99%

Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms

Nur Atiqah Rochin Demong,

Melissa Shahrom,

Ramita Abdul Rahim

et al. 2023

JICT

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The global trend of student social well-being has steadily declined in recent years. As a result, the need for a personalized recommendation classification model that can accurately assess and identify the individual student’s social well-being has become increasingly important. This article will discuss the development of an adaptive personalized recommendation classification model for students’ social well-being based on personality trait determinants. Social well-being is a field that analyses society, individual behavioural patterns, behavioural networks, and cultural elements of daily life. Social well-being develops critical thinking by understanding the social frameworks that affect humans by exposing the social basis of daily actions. For instance, when students are pleased, their academic achievement, behaviour, social integration, and happiness improve. This study classifies the effects of the Big 5 Personality Traits (Extraversion, Openness, Agreeableness, Emotional Stability, and Conscientiousness) on students’ Industry 4.0 Social Well-being levels by analyzing their demographic and personality traits. A dataset was gathered through a survey distributed to students in a selected institution. The classifier’s accuracy was assessed using the WEKA tool on a data set of 286 occurrences and 19 traits, and a confusion matrix was constructed. After analyzing the results of all algorithms, it was determined that the IBk and Randomizable Filtered Classifier algorithms give the best accuracy on social well-being readiness, with a comparable percentage value of 91.26%. The agreeableness personality trait, which represents a person’s level of pleasantness, politeness, and helpfulness, had the greatest influence on the social well-being of the students. They have a positive outlook on human behaviour and get along well with others. Since social well-being contributes to a person’s increased quality of life and happiness, improving students’ current quality of life would lead to the development of a social parameter that can assess the growth of a country and the increased happiness offamilies and communities. Personality traits models have become an increasingly important tool for understanding and predicting human behavior. By analyzing different personality trait models, we can gain insights into how accurately and reliably they can predict individual behavior. This is especially useful in fields such as psychology, marketing, and recruitment, where understanding the nuances of individual personalities can be critical to success. In this study, how different personality trait models compare in terms of accuracy and reliability is explored using different machine learning algorithms using the WEKA tool. Personality trait models are increasingly being used to measure social well-being. This model is based on the idea that individuals’ personalities are composed of a set of underlying traits which can be measured and compared. By understanding these traits, we can better understand the students’ social well-being and how the environment around them may impact it.

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Section: Related Workmentioning

confidence: 99%

Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms

Nur Atiqah Rochin Demong,

Melissa Shahrom,

Ramita Abdul Rahim

et al. 2023

JICT

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“…This stage is performed using the saurus English lexicon [18] and Enneagram ontology [19], [20] The personality detection phase uses a statistical technique to calculate the personality's probability distribution for each one. The largest probability percentage is the detected personality [21]. The approach uses Twitter Text.…”

Section: Introductionmentioning

confidence: 99%

“…Enneagram's personalities probability distribution is computed for each of the nine personalities. The highest probability distribution for a personality means that it is the detected one [21].…”

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confidence: 99%

Approach for Enneagram personality detection for Twitter text: a case study

Abdelhamid,

Ismail,

Aref

2023

IJECE

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Understanding people’s emotions and orientations attracts researchers nowadays. Current personality detection research concentrates on models such as the big five model, the three-factor model. The Enneagram is deeper than these models for providing a comprehensive view. This theory is a unique personality model because it illustrates what drives human behavior. This recognition helps in building smarter recommendation systems and intelligent educational systems. Enneagram personalities are realized through a long questionnaire-based test. People are not concerned about doing a test because it is time-consuming. A proposed case study employs Twitter’s text to detect Enneagram personality because it requires no time or effort. The proposed case study is based on an approach that uses a combination of ontology, lexicon, and statistical technique. This proposed case study uses the biography description text and 40 tweets of a Twitter profile text. The highest probability percentage is peacemaker personality which is 15.58%. This result means that the identified personality is the peacemaker. The outcome is equivalent to the determination of the Enneagram’s specialized people. This result promises more positive outcomes. This is the first automated approach to determine the Enneagram from text.

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Architecture for Personality Detection Using Enneagram Knowledge: Case Study

Cited by 2 publications

References 18 publications

Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms

Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms

Approach for Enneagram personality detection for Twitter text: a case study

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