Personality Classification through Social Media Using Probabilistic Neural Network Algorithms

Sarwani, Mohammad Zoqi; Sani, Dian Ahkam; Fakhrini, Fitria Chabsah

doi:10.25139/ijair.v1i1.2025

Cited by 7 publications

(6 citation statements)

References 3 publications

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Section: Methodsmentioning

confidence: 99%

“…In the study [14][15], Facebook status information can be used to find out a person's personality, especially for student tests, making it easier and shortening the time for personality student. In [14] research, the algorithm used is PNN. The accuracy rate obtained was 60.00% from 25 respondents in his research, with 10 data training and 15 data testing.…”

Section: Methodsmentioning

confidence: 99%

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Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Sarwani

Sani

2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Sarwani

Sani

2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…looking at Twitter analysis research to find out the character of someone using the Naïve Bayes Classifier. The results of this study are the development of previous research, which uses the Probabilistic Neural Network algorithm to classify one's personality through Facebook social media [6].…”

mentioning

confidence: 87%

Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

Sarwani¹,

Salafudin²,

Sani³

2020

Int.J.Artif.Intell.Robot.

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With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.

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“…Souri et al (2018) represented a model based on boosting-decision tree to recognize user personality by their profile on Facebook. Sarwani et al (2019) using Facebook status document developed the approach to predict The Big Five personality. In addition to developing prediction tools, machine learning and text mining techniques were also applied to the field of personality assessment.…”

Section: Text Mining and Personality Assessmentmentioning

confidence: 99%

Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining

Zhu

Zhou

et al. 2021

Front. Psychol.

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This prospective study was designed to propose a novel method of assessing proactive personality by combining text mining technology and Item Response Theory (IRT) to measure proactive personality more efficiently. We got freely expressed texts (essay question text dataset and social media text dataset) and item response data on the topic of proactive personality from 901 college students. To enhance validity and reliability, three different approaches were employed in the study. In Method 1, we used item response data to develop a proactive personality evaluation model based on IRT. In Method 2, we used freely expressed texts to develop a proactive personality evaluation model based on text mining. In Method 3, we utilized the text mining results as the prior information for the IRT estimation and built a proactive personality evaluation model combining text mining and IRT. Finally, we evaluated those three approaches via the confusion matrix indicators. The major result revealed that (1) the combined method based on essay question text, micro-blog text with pre-estimated IRT parameters performed the highest accuracy of 0.849; (2) the combined method using essay question text and pre-estimated IRT parameters performed the highest sensitivity of 0.821; (3) the text classification method based on essay question text had the best performance on the specificity of 0.959; and (4) if the models were considered comprehensively, the combined method using essay question text, micro-blog text, and pre-estimated IRT parameters achieved the best performance. Thus, we concluded that the novel combined method was significantly better than the other two traditional methods based on IRT and text mining.

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Personality Classification through Social Media Using Probabilistic Neural Network Algorithms

Cited by 7 publications

References 3 publications

Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining

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