Developing Deep Learning Models for Depression Detection in Texts
| dc.contributor.advisor | Eick, Christoph F. | |
| dc.contributor.committeeMember | Chen, Guoning | |
| dc.contributor.committeeMember | Tsekos, Nikolaos V. | |
| dc.contributor.committeeMember | Yuan, Xiaojing | |
| dc.creator | Aigbe, Steve Aibuedefe | |
| dc.date.accessioned | 2024-01-27T01:54:06Z | |
| dc.date.created | December 2023 | |
| dc.date.issued | 2023-12 | |
| dc.date.updated | 2024-01-27T01:54:07Z | |
| dc.description.abstract | Depression is a major mental health disorder affecting a significant portion of the world population. Methods mostly being employed for depression detection are clinical interviews and questionnaire surveys where psychiatric assessment tables are used to establish mental disorder prognosis. Analyzing texts written by an individual can serve as an additional knowledge source to diagnose depression. Consequently, using deep learning models to detect depressed and non-depressed individuals based on social media posts, by analyzing the words being posted, has become the focus of recent research. The lack of big-sized depression-labeled datasets for training models for depression detection in texts is a major challenge. Also, selecting a data augmentation (DA) method to augment the available small-sized datasets is difficult. So, we developed a methodology, named DAMEVAL, for the evaluation of DA methods for text classification. In DAMEVAL, we proposed a set of evaluation measures and benchmark NLP datasets for the evaluation and comparison of DA methods to create a reference for easier selection of DA methods by users. In this dissertation, we extracted and analyzed the textual depression symptoms indicators present in texts posted in online forums and the distribution of these indicators with respect to depressed and non-depressed social media users. Also, we computed weights, using the TFIDF method, based on the extracted depression symptoms’ indicators present in users' posts. Subsequently, we introduced a weighted deep learning model named DEP-BERTCNN, based on the computed depression indicators’ weights, for depression detection in text in online forums. DEP-BERTCNN uses a combination of a pre-trained BERT language model, an attention model and convolutional neural network to classify forum users as depressed or non-depressed. The DEP-BERTCNN model was trained and evaluated on the large-scale Reddit Self-reported Depression Dataset (RSDD). Our results outperform several baseline methods for depression detection in texts, demonstrating the effectiveness of combining deep learning model with linguistic indicators associated with depression symptoms. In summary, we aim to develop a beneficial system that can easily be used to detect depression in texts and enable policy makers to respond to mental health escalations easily and promptly. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Aigbe, Steve Aibuedefe, and Christoph Eick. "Learning domain-specific word embeddings from covid-19 tweets." In 2021 IEEE International Conference on Big Data (Big Data), pp. 4307-4312. IEEE, 2021. | |
| dc.identifier.uri | https://hdl.handle.net/10657/16219 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Depression Detection | |
| dc.subject | BERT | |
| dc.subject | CNN | |
| dc.subject | Attention | |
| dc.subject | Weights | |
| dc.subject | TF-IDF | |
| dc.subject | Texts | |
| dc.subject | NLP | |
| dc.title | Developing Deep Learning Models for Depression Detection in Texts | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2025-12-01 | |
| local.embargo.terms | 2025-12-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |