Big Five Personality Prediction from Social Media Data using Machine Learning Techniques 
Suman Maloji1, Kasiprasad Mannepalli2, Navya Sravani. J3, K. Bhavya Sri4, C. Sasidhar5
1Dr. Suman Maloji*, Professor,Department of Electronics and Communication Engineering.
2Dr. Kasiprasad Mannepalli, Associate Professor, Electronics and Communication Engineering Department.
3Navya Sravani. J, Student, Electronics and Communication Engineering Department, Koneru Lakshmaiah Education Foundation.
4K. Bhavya Sri, Student, Electronics and Communication Engineering Department, Koneru Lakshmaiah Education Foundation.
5C.Sasidhar, Student, Electronics and Communication Engineering Department, Koneru Lakshmaiah Education Foundation.
Manuscript received on March 29, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 2412-2417 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7946049420/2020©BEIESP | DOI: 10.35940/ijeat.D7946.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Personality has been important for a number of types of cooperation; it has useful in predicting job achievement, expert and emotional relationship achievement, and even tendency towards a variety of interfaces. To accurately examine the characters of users, a personality test must be carried out. In numerous areas of online life it is usually impractical to use character research. . We used SVM classification, Random Forest algorithm, Naïve Bayes Algorithm and Logistic regression to comparatively predict the user’s personality accurately. The main goal of the paper is to evaluate the machine learning models using the four parameters- accuracy, precision, recall, f1 score and basing upon these parameters the best machine learning model will be used to classify the big five personality traits of the twitter users.
Keywords: Social Media, Twitter, Personality, Feature extraction.