Personality Prediction Based on Twitter Information in Bahasa Indonesia

Ong, Veronica; Rahmanto, Anneke D. S.; Williem,; Suhartono, Derwin; Nugroho, Aryo; Andangsari, Esther Widhi; Suprayogi, Muhamad Nanang

doi:10.15439/2017f359

Cited by 25 publications

(14 citation statements)

References 23 publications

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“…The evaluation metrics used are precision, recall, and f-measure. We do not use accuracy to prevent accuracy paradox [21,22] and therefore not using baseline value as it displays accuracy.…”

Section: Methodsmentioning

confidence: 99%

Identifying Personality Traits for Indonesian User from Twitter Dataset

Jeremy¹,

Prasetyo²,

Suhartono³

2019

IJFIS

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Social media allows the user to convey their actual self and share their life experiences through numerous ways. This behavior in turn reflects the user's personality. In this paper, we experiment to automatically predict user's personality based on Big Five Personality Trait on Twitter. Our focus is towards Indonesian user. Not only word n-gram, Twitter metadata is also used in a certain combination to determine the feature that will be used to predict the personality. Our research also attempts to find optimum setting based on the number of n-gram, classifier, and twitter metadata. Our experiment yields 0.7482 at most on F-Measure. We conclude that among all scenario, twitter metadata is the least impactful feature, while word n-gram impacts the most.

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Section: Methodsmentioning

confidence: 99%

Identifying Personality Traits for Indonesian User from Twitter Dataset

Jeremy¹,

Prasetyo²,

Suhartono³

2019

IJFIS

Self Cite

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show abstract

“…It seems good accuracy, but this works a tiny scope which is only 60 users. Ong [10] also developed an Indonesian-language of Big Five personality classification system. There are 12 feature selections, namely, the number of tweets, followers, following, favorites, retweets, tweet retweets, quote tweets, mentions, replies, hashtags, extracted tweet URL form, and the time difference between each tweet.…”

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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“…All datasets were composed by randomly selecting the determined number of text documents from the whole set of texts. 4 The experiments with AGE and GENDER dimensions were performed with relevant datasets (described in Section III-A) of different sizes, containing 100, 300, 500, 1,000, 2,000, and 5,000 instances in each class.…”

Section: A Datasetsmentioning

confidence: 99%

“…The majority of AP tasks are solved with the traditional machine learning methods and the weight vectors of features [3], [4]. The most influential examples of this field refer to Support Vector Machines (SVMs) [5], Multi-Class Real Winnow [6], Mean Proximity Clustering [7] and Holomorphic Transforms [8].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Lithuanian Author Profiling with the Deep Learning

Kapociute-Dzikicne

Damaševičius

2018

Annals of Computer Science and Information Systems

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We address the Lithuanian author profiling task in two dimensions (AGE and GENDER) using two deep learning methods (i.e., Long Short-Term Memory-LSTM) and Convolutional Neural Network-CNN) applied on the top of Lithuanian neural word embeddings. We also investigate an impact of the training dataset size on the author profiling accuracy. The best results are achieved with the largest datasets, containing 5,000 instances in each class. Besides, LSTM was more effective on the smaller datasets, and CNN-on the larger ones. We compare the deep learning methods with the traditional machine learning methods (in particular, Naive Bayes Multinomial and Support Vector Machine), and frequencies of elements as the feature representation). The comparison revealed that the deep learning is not the best solution for our author profiling task.

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Personality Prediction Based on Twitter Information in Bahasa Indonesia

Cited by 25 publications

References 23 publications

Identifying Personality Traits for Indonesian User from Twitter Dataset

Identifying Personality Traits for Indonesian User from Twitter Dataset

Word Analysis of Indonesian Keirsey Temperament

Lithuanian Author Profiling with the Deep Learning

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