Psychological Understanding of Textual journals using Natural Language Processing approaches

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 trade-off 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.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2022

Author dc:creator

• Kazemeinizadeh, Amirmohammad

Advisor dc:contributor.advisor

• Robert E. Mercer

• personality detection
• sleep health
• topic modeling
• text classification
• sentiment analysis
• interpretable artificial intelligence

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/32434

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/32434