Application of classical and advanced machine learning models to predict personality on social media

Knowing personality traits and how people tend to think, feel and behave has been always an appealing and studied topic. This interest together with the vast amount of data generated every day on social networks present an ideal scenario to address this problem. By properly processing this data, it could be useful for many aspects of people's daily life. In this study, we applied different Machine Learning methods to solve this problem using a dataset labelled with the MBTI personalities, and we compared several algorithms such as Nai've Bayes, Logistic Regression and three different Artificial Neural Networks. Two main experiments were conducted. First, a clustering-oriented solution. Second, a classification approach. The latter turned out to outperform the clustering methods. On average, our models achieved around 90% accuracy. Finally, in order to show an example of our solution, we will validate our model with the latest news about COVID-19 and the La Palma Volcano.

Automated summary

In this study, different Machine Learning methods were applied to solve the problem of personality identification using a dataset labelled with the MBTI personalities. Comparing several algorithms, it was found that the classification approach outperformed the clustering methods with an average accuracy of around 90%. Finally, the model will be validated with the latest news about COVID-19 and the La Palma Volcano.