Sleep health

Psychological Understanding of Textual journals using Natural Language Processing approaches

Recent advancements in artificial intelligence models that accept textual inputs are becoming more and more accurate. However, because of the differences between the nature of the artificial intelligence models and human functioning, understanding the AI outputs are becoming harder for humans. In this project, the aim is to utilize top AI models in the field of natural language processing to provide meaningful insight from psychological real-world documents that contain complex structures. The project involves two main chapters each including a different dataset. The first chapter is related to binary classification on a personality detection dataset, while the second one is about sentiment analysis and Topic Modeling of sleep-related reports.