Psychological Understanding of Textual journals using Natural Language Processing approaches
Year: 2022
Type: dissertation
Abstract: Recent NLP advancements have improved the state-of-the-art in well-known datasets and are appealing more attention day by day.However, as the models become more complicated, the ability to provide interpretable and understandable results is becoming harder so the tradeoff between accuracy and interpretability is a concern that is yet to be addressed.In this project, the aim is to utilize state-of-the-art NLP models 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.
Author: Kazemeinizadeh, Amirmohammad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6986675929