Abstract:
Social networking sites have become the most popular Internet destination, giving social
scientists a unique chance to study online behavior. A rising number of study articles on
social media are being published, with just a few of them focusing on personality
prediction. Personality assessments computer based on data from social media platforms
has proven to be more accurate than judgments given by persons who are familiar with the
topic. Text on social networking sites is used to automatically detect an individual's
personality qualities. We are using the Myers-Briggs Type Indicator (MBTI) dataset. These
datasets have 16 types and 8675 posts. From the input text, we categorized four personality
qualities using the Myers-Briggs Type Indicator. They are, in particular IntroversionExtroversion (I-E), Intuitions-Sensing(N-S), Feeling-Thinking(F-T), and JudgingPerceiving(J-P) . This data set we collected from Kaggle. Firstly, preprocessing the dataset.
This is text data so that we are using NLP for preprocessing. Then using the machine
learning techniques. Tokenization, word stemming, stop words deletion and feature, as well
as TF IDE, are examples of text preprocessing methods. We are using six machine learning
algorithm. We compare all of algorithm, among them the Support Vector Machine (SVM)
have best for highest accuracy. Support vector machines outperform the other six machine
learning algorithms in terms of accuracy, according to the results of an experiment.
