Myers-briggs Personality Prediction and Sentiment Analysis of Twitter using Machine Learning Classifiers and BERT

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Author(s)

Prajwal Kaushal 1,* Nithin Bharadwaj B P 1 Pranav M S 1 Koushik S 1 Anjan K Koundinya 1

1. Department of Computer Science and Engineering, BMS Institute of Technology and Management, Bengaluru, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2021.06.04

Received: 14 Jul. 2021 / Revised: 16 Aug. 2021 / Accepted: 9 Oct. 2021 / Published: 8 Dec. 2021

Index Terms

BERT, MBTI, Machine Learning, Personality Prediction, Sentiment Analysis

Abstract

Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.

Cite This Paper

Prajwal Kaushal, Nithin Bharadwaj B P, Pranav M S, Koushik S, Anjan K Koundinya, "Myers-briggs Personality Prediction and Sentiment Analysis of Twitter using Machine Learning Classifiers and BERT", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.6, pp.48-60, 2021. DOI:10.5815/ijitcs.2021.06.04

Reference

[1]Golam Mostafa, Ikhtiar Ahmed, Masum Shah Junayed, "Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.2, pp.38-48, 2021. DOI: 10.5815/ijitcs.2021.02.04
[2]Poornima A, K Sathiya Priya, “A Comparative Sentiment Analysis of Sentence Embedding Using Machine Learning Techniques”, 6th International Conference on Advanced Computing & Communication Systems (ICACCS), March 2020.
[3]Deep Kaneria, Brijesh Patel, “Sentiment Analysis for Twitter Data”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-9 Issue-7S, May 2020.
[4]Junchao Dong, Feijuan He, Yunchuan Guo, Huibing Zhang, “A Commodity Review Sentiment Analysis Based on BERT- CNN Model”, 5th International conference on Computer and communication Systems, ISBN:978-1-7281-6137-2, May 2020,
[5]ZHENGJIE GAO, AO FENG, XINYU SONG, AND XI WU “Target-Dependent Sentiment Classification With BERT”, accepted October 5, 2019, date of publication October 11, 2019, date of current version November 4, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2946594
[6]Mickel Hoang, Oskar Alija Bihorac “Aspect-Based Sentiment Analysis Using BERT”, Issue 167, Article – 20, ISSN: 1650- 3740, 2019.
[7]A Brahmananda Reddy, D.N. Vasundhara, P. Subhash “Sentiment Research on Twitter Data” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S11, September 2019
[8]Sahar A. El_Rahman, Feddah Alhumaidi AlOtaibi, Wejdan Abdullah AlShehri “Sentiment Analysis of Twitter Data”, Pulished in International Conference on Computer and Information Sciences (ICCIS) April 2019.
[9]Prakruthi V, Sindhu D, Dr S Anupama Kumar, “Real Time Sentiment Analysis of Twitter Posts”, 3rd IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, December 2018, IEEE.
[10]Vishal S. Shirsat, Rajkumar S. Jagdale, S. N. Deshmukh, “Document Level Sentiment Analysis for News Articles”, International Conference on Computing, Communication, Control and Automation (ICCUBEA), August 2017, IEEE.
[11]Abdullah Asaeedi, Mohammad Zubair Khan, “A Study on Sentiment Analysis Techniques of Twitter Data” International Journal of Advanced Computer Science and Applications, Vol.10, No.2, 2019.
[12]Indhra om Prabha M, G. Umarani Srikanth “Survey of Sentiment Analysis Using Deep Learning Techniques”, Published in 1st International Conference on Innovations in Information and Communication Technology (ICIICT),2019
[13]Vasanthakumar G U, Shashikumar D R, Suresh L, “Profiling Social Media Users, a Content-Based Data Mining Technique for Twitter Users” 1st International Conference on Advances in Information Technology (ICAIT), 2019.
[14]P. S. Dandannavar, S. R. Mangalwede and P. M. Kulkarni, "Social Media Text - A Source for Personality Prediction," 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018.
[15]P. B. Kollipara, L. Regalla, G. Ghosh and N. Kasturi, "Selecting Project Team Members through MBTI Method: An Investigation with Homophily and Behavioural Analysis," 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, pp. 1-9, doi: 10.1109/ICACCP.2019.8883022
[16]Shristi Chaudhary, Ritu Singh, Syed Tausif Hasan, Ms. Inderpreet Kaur, “A Comparative Study of Different Classifiers for Myers Brigg Personality Prediction Model”, International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 05, May-2018
[17]Medhavini Rao, Pooja Jayant Kanchugar, Pooja R, Prakshitha M N, Anitha R, “Personality Recognition using Social Media Data”, International Research Journal of Engineering and Technology (IRJET), Volume: 06 Issue:04, April-2019
[18]Hernandez Rayne, Knight Ian Scott, “Predicting Myers-Briggs Type Indicator with Text Classification”, 31st Conference on Neural Information Processing Systems (NIPS), 2017
[19]Azhar Imran, Muhammad Faiyaz, Faheem Akhtar,"An Enhanced Approach for Quantitative Prediction of Personality in Facebook Posts", International Journal of Education and Management Engineering(IJEME), Vol.8, No.2, pp.8-19, 2018.DOI: 10.5815/ijeme.2018.02.02
[20]Munir Ahmad, Shabib Aftab, "Analyzing the Performance of SVM for Polarity Detection with Different Datasets", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.10, pp. 29-36, 2017.DOI: 10.5815/ijmecs.2017.10.04
[21]“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova Google AI Language
[22]Large Movie Review Dataset for Sentiment Classification, https://ai.stanford.edu/~amaas/data/sentiment/
[23]Mitchell J, “MBTI, Myers-Briggs Personality Type Dataset”, 2017.