Utilizing social media and machine learning for personality and emotion recognition using PERS

Personality reflects how people can behave in different situations and affects their decisions. Analyzing personality is useful in many fields, for example in the prediction of performance in a job. Emotion recognition is another important research topic due to the wide spread of social media. People express their feeling in form of Facebook posts, tweeter real reactions, and shares. Understanding both personality and emotions from the written text is much easier when it comes to humans. However, this task is impossible with the huge amount of data spread all other social media. The use of machine learning algorithms for personality and emotion recognition from text data is a new research field. In this paper, we propose an enhanced recognition system for personality recognition and emotion recognition. The proposed enhanced recognition system is composed of four main modules, namely data acquisition module, data preprocessing module, personality recognition module, and emotion recognition module. Several machine learning algorithms are used for the multiclass classification process. Gray wolf optimization (GWO) algorithm is used for hyperparameter optimization, while group GWO (GGWO) algorithm is used for feature selection. The proposed model could achieve an accuracy of 99.99% using the random forest algorithm for personality detection and 88.06% using a decision tree for emotion recognition, which outperforms other state-of-the-art studies. We can profit from social media despite some of its drawbacks by understanding people's emotions through their tweets, posts, etc. For instance, before someone commits suicide, we can tell what their intentions are. Most suicide committers, according to recent studies, leave suicide notes on their social media accounts, and these letters need to be taken seriously.