Implementation of Decision Tree C4.5 for Big Five Personality Predictions with TF-RF and TF-CHI2 on Social Media Twitter

Willy,; Setiawan, Erwin Budi; Nugraha, Fida Nirmala

doi:10.1109/ic3ina48034.2019.8949601

Cited by 10 publications

(15 citation statements)

References 4 publications

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“…The authors stated that the low accuracy score is due to the imbalanced data. Similar study conducted by [7], implemented Decision Tree C4.5 method and TF-RF and TF-CHI 2 as the linguistic approach for personality prediction and achieved 65.72% accuracy score on 145 Twitter users with total of tweets data as many as 331.439 tweets data. The accuracy of 65.72% was obtained by combining social features and TF-RF as the linguistic approach.…”

Section: Introductionmentioning

confidence: 87%

“…The dataset size in this research is larger than previous researches with 295 Twitter users. Our main reference is a previous study by [7], the accuracy scores they obtained are still low. Their research only uses social features data with term weighting as the linguistic approach.…”

Section: Introductionmentioning

confidence: 99%

“…As for the solution to the imbalance data problem, this research will implement SMOTE as an oversampling technique. The previous research by [7] and [6], had only experimented on one method. Therefore, the innovation of this research is we conducted an experiment to combine SVM and SMOTE with BERT as the semantic approach on personality prediction system.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Approach for Big Five Personality Prediction on Twitter

Salsabila

Setiawan

2021

RESTI

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Personality provides a deep insight of someone and has an important part in someone’s job performance. Predicting personality through social media has been studied on several research. The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personality on Twitter users and increase the performance of the personality prediction system. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the personality prediction system.

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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Approach for Big Five Personality Prediction on Twitter

Salsabila

Setiawan

2021

RESTI

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“…Willy et al in 2019 [7] have implemented C4.5 Decision Tree for classification of personality based on the Big Five Model. They have used the Twitter API to obtain the dataset that is roughly around 110 million tweets every day.…”

Section: Related Workmentioning

confidence: 99%

Big Five Personality Detection Using Deep Convolutional Neural Networks

Tinwala¹,

Rauniyar²

2021

Preprint

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Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

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“…F1-Score F1 Score is the weighted average of Precision and Recall Source: Willy [16] Where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.…”

mentioning

confidence: 99%

Word Analysis of Indonesian Keirsey Temperament

Iskandar¹,

Utami²,

Prasetio³

2020

Indonesian J. Comput. Cybern. Syst.

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Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%

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Implementation of Decision Tree C4.5 for Big Five Personality Predictions with TF-RF and TF-CHI2 on Social Media Twitter

Cited by 10 publications

References 4 publications

Semantic Approach for Big Five Personality Prediction on Twitter

Semantic Approach for Big Five Personality Prediction on Twitter

Big Five Personality Detection Using Deep Convolutional Neural Networks

Word Analysis of Indonesian Keirsey Temperament

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