Stress Detection with Natural Language Processing Techniques from Social Media Articles

Abstract

Stress have significant effects on health and well-being. Therefore, NLP's ability to identify stress situations is critical to providing early intervention and support. NLP can extract meaningful clues from people's online communications and written texts; This makes it a powerful tool for detecting signs of stress. Identifying signs of stress early can help individuals improve their ability to cope with stress by providing appropriate support and guidance; NLP can automate this process. Considering these situations, this study tried to detect the stress states of the users using the Stress Detection from Social Media Articles dataset. Generative Pre-trained Transformer (GPT), Logistic Regression (LR) and Artificial Neural Network (ANN) methods were used for classification. Doc2vec method was used to convert texts into vector data. In the classification processes, the highest classification success was obtained from the ANN model with 80.34%. It is thought that this success can be increased with more data and different models.