Abstract
The Myers–Briggs Personality Type Indicator (MBTI) is used to identify people’s personalities using a questionnaire, which takes a long time and effort. Due to the abundance of data about a person present in social networks, finding their type utilizing this data will yield better results. By using machine learning techniques, this data can be used to identify various aspects of a person’s personality. The MBTI dataset from Kaggle was used for building the model. The naive Bayes technique is adapted for making the classification model that gives a person’s MBTI type. Synthetic Minority Over-Sampling Technique (SMOTE) and random under-sampling are used to sample the data to attain a balance. The hundred recent tweets of the user are scraped, preprocessed, and applied to the model, which gives their MBTI type and its description.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kunte AV, Panicker S (2019) Using textual data for personality prediction: a machine learning approach. In: 4th international conference on information systems and computer networks (ISCON), India
Jeremya NH, Suhartonoa D (2020) Automatic personality prediction from Indonesian user on Twitter using word embedding and neural networks. Procedia Comput Sci 179:416–422
Amirhosseini MH, Kazemian H (2020) Machine learning approach to personality type prediction based on the Myers-Briggs type indicator. Multimodal Technol Interact 4(1)
Aung ZMM, Myint PH (2019) Personality prediction based on content of Facebook users: a literature review. In: 20th IEEE/ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing (SNPD), Japan
Utamia NA, Maharani W, Atastinaa I (2020) Personality classification of Facebook users according to big five personality using SVM (Support Vector Machine) method. Procedia Comput Sci 179:177–184
Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58(3)
Zhao J, Zeng D, Xiao Y, Che L, Wang M (2020) User personality prediction based on topic preference and sentiment analysis using LSTM model. Pattern Recogn Lett 138:397–402
Maliki I, Sidik MA (2020) Personality prediction system based on signatures using machine learning. In: IOP conference series, materials science and engineering, 879
Zhu Y (2020) The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model. Int J Electr Eng Educ 0020720920936839
Christian H, Suhartono D, Chowanda A, Zamli KZ (2021) Text-based personality prediction from multiple social media data sources using pre-trained language model and model averaging. J Big Data 8(1):1–20
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kishore Kumar, R., Jeeva Surya, V., Shana, J. (2022). Personality Prediction Based on Twitter Tweets. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-8225-4_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8224-7
Online ISBN: 978-981-16-8225-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)