Identifying Personality Traits for Indonesian User from Twitter Dataset

Jeremy, Nicholaus Hendrik; Prasetyo, Cristian; Suhartono, Derwin

doi:10.5391/ijfis.2019.19.4.283

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…At the initiation stage, data collection was carried out to increase the amount of Twitter data that had been collected from previous studies [3,19,27]. Twitter data that has been collected manually will be annotated with the help of a psychological expert to define the personality of each Twitter user.…”

Section: Methodsmentioning

confidence: 99%

“…This is caused by due to small number of dataset used in this study to capture much more contextual information in creating generalized model. Meanwhile, another study using Twitter dataset in Bahasa which were carried out by [3,19,31] used a different algorithm in building the personality prediction model. In feature extraction methods, the researcher was assessing the tendency user choice of words by using n-gram and LIWC.…”

Section: Related Workmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

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 details for each phase can be seen in figure 1. For the initial phase, data collected from previous research [9,11,12] has been collected for Twitter dataset. The label for defining the Big Five traits has been annotated by physiological experts.…”

Section: Methodsmentioning

confidence: 99%

“…Their research uses SGD (Stochastic Gradient Descent), XGBoost, and super learner to produce a good ROC-AUC (Receiver Operating Characteristic and Area Under Curve) score. Research conducted by [12] also uses Twitter dataset with more data that consists of tweets from 508 users. This research use word-n-gram and Twitter metadata to process using Random Forest classifier to produce 0,744 f1 scores on average.…”

Section: Related Workmentioning

confidence: 99%

Personality Identification from Social Media Using Ensemble Bert and Roberta

Tsani,

Suhartono

2023

IJCAI

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Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media data to conduct research about personality identification. The identification result can be used as a parameter to screen candidate attitudes in the company's recruitment process. Some approaches were used for research about personality; one of the most popular is the Big Five Personality Model. In this research, an ensemble model between BERT and RoBERTa was introduced for personality prediction from the Twitter and Youtube datasets. The data augmentation method also introduces to handling the imbalance class for each dataset. Pre-trained model BERT and RoBERTa was used as the feature extraction method and modeling process. To predict each trait in the Big Five Personality, the voting ensemble from BERT and RoBERTa achieved an average f1 score 0,730 for Twitter dataset and 0,741 for Youtube dataset. Using the proposed model, we conclude that data augmentation can increase average performance compared to the model without data augmentation process.Povzetek: Članek uvaja model združevanja siestemov BERT in RoBERTa za napovedovanje osebnosti iz podatkov Twitterja (X) in Youtube, z izboljšanjem s pomočjo podatkovne augmentacije.

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“…In the classification of the Big Five Personality, which was done by Jeremy [11], there is an addition of 4 feature extraction approaches. This research-based on metadata approaches such as the number of followers, following, tweets, favorites, retweets, mentions, quotes, replies, and hashtags.…”

Section: Previous Workmentioning

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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Identifying Personality Traits for Indonesian User from Twitter Dataset

Cited by 13 publications

References 20 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

Personality Identification from Social Media Using Ensemble Bert and Roberta

Word Analysis of Indonesian Keirsey Temperament

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