Personality Classification through Social Media Using Probabilistic Neural Network Algorithms

Mohammad Zoqi Sarwani, Dian Ahkam Sani, Fitria Chabsah Fakhrini

Abstract


Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.


Keywords


text mining; text processing; term inverse document frequent; probabilistic neural network.

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DOI: http://dx.doi.org/10.25139/ijair.v1i1.2025

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Copyright (c) 2019 Mohammad Zoqi Sarwani, Dian Ahkam Sani, Fitria Chabsah Fakhrini

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International Journal of Artificial Intelligence & Robotics (IJAIR)
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