Multi task opinion enhanced hybrid BERT model for mental health analysis
Abstract Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument for assessing emotional well-being. Most current models neglect the significance of integrating viewpoints i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86124-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571733153939456 |
|---|---|
| author | Md. Mithun Hossain Md. Shakil Hossain M. F. Mridha Mejdl Safran Sultan Alfarhood |
| author_facet | Md. Mithun Hossain Md. Shakil Hossain M. F. Mridha Mejdl Safran Sultan Alfarhood |
| author_sort | Md. Mithun Hossain |
| collection | DOAJ |
| description | Abstract Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument for assessing emotional well-being. Most current models neglect the significance of integrating viewpoints in comprehending mental health in favor of single-task learning. To offer a more thorough knowledge of mental health, in this study, we present an Opinion-Enhanced Hybrid BERT Model (Opinion-BERT), built to handle multi-task learning for simultaneous sentiment and status categorization. With the help of TextBlob and SciPy, we extracted opinions and dynamically constructed new opinion embeddings to complement the pre-trained BERT model. Using a hybrid architecture, these embeddings are integrated with the contextual embeddings of BERT, whereby the CNN and BiGRU layers collected local and sequential characteristics. This combination helps our model to identify and categorize user status and attitudes from the text more accurately, which leads to more accurate mental health assessments. When we compared the performance of Opinion-BERT to some baseline models, including BERT, RoBERTa, and DistilBERT, we found that it performed much better. Opinion-enhanced embeddings are crucial for improving performance, as demonstrated by our multi-task learning framework’s 96.77% sentiment classification accuracy of 94.22% status classification accuracy. This work provides a more nuanced understanding of emotions and psychological states by demonstrating the potential of combining opinion and sentiment data for mental health analysis in a multi-task learning environment. |
| format | Article |
| id | doaj-art-78089438cb304167bc37c48e7438815e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-78089438cb304167bc37c48e7438815e2025-02-02T12:23:03ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-86124-6Multi task opinion enhanced hybrid BERT model for mental health analysisMd. Mithun Hossain0Md. Shakil Hossain1M. F. Mridha2Mejdl Safran3Sultan Alfarhood4Department of Computer Science and Engineering, Bangladesh University of Business and TechnologyDepartment of Computer Science and Engineering, Bangladesh University of Business and TechnologyDepartment of Computer Science, American International University-BangladeshResearch Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityAbstract Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument for assessing emotional well-being. Most current models neglect the significance of integrating viewpoints in comprehending mental health in favor of single-task learning. To offer a more thorough knowledge of mental health, in this study, we present an Opinion-Enhanced Hybrid BERT Model (Opinion-BERT), built to handle multi-task learning for simultaneous sentiment and status categorization. With the help of TextBlob and SciPy, we extracted opinions and dynamically constructed new opinion embeddings to complement the pre-trained BERT model. Using a hybrid architecture, these embeddings are integrated with the contextual embeddings of BERT, whereby the CNN and BiGRU layers collected local and sequential characteristics. This combination helps our model to identify and categorize user status and attitudes from the text more accurately, which leads to more accurate mental health assessments. When we compared the performance of Opinion-BERT to some baseline models, including BERT, RoBERTa, and DistilBERT, we found that it performed much better. Opinion-enhanced embeddings are crucial for improving performance, as demonstrated by our multi-task learning framework’s 96.77% sentiment classification accuracy of 94.22% status classification accuracy. This work provides a more nuanced understanding of emotions and psychological states by demonstrating the potential of combining opinion and sentiment data for mental health analysis in a multi-task learning environment.https://doi.org/10.1038/s41598-025-86124-6Opinion-BERTOpinions embeddingHybrid BERTMental health sentiment analysisMulti-task learning |
| spellingShingle | Md. Mithun Hossain Md. Shakil Hossain M. F. Mridha Mejdl Safran Sultan Alfarhood Multi task opinion enhanced hybrid BERT model for mental health analysis Scientific Reports Opinion-BERT Opinions embedding Hybrid BERT Mental health sentiment analysis Multi-task learning |
| title | Multi task opinion enhanced hybrid BERT model for mental health analysis |
| title_full | Multi task opinion enhanced hybrid BERT model for mental health analysis |
| title_fullStr | Multi task opinion enhanced hybrid BERT model for mental health analysis |
| title_full_unstemmed | Multi task opinion enhanced hybrid BERT model for mental health analysis |
| title_short | Multi task opinion enhanced hybrid BERT model for mental health analysis |
| title_sort | multi task opinion enhanced hybrid bert model for mental health analysis |
| topic | Opinion-BERT Opinions embedding Hybrid BERT Mental health sentiment analysis Multi-task learning |
| url | https://doi.org/10.1038/s41598-025-86124-6 |
| work_keys_str_mv | AT mdmithunhossain multitaskopinionenhancedhybridbertmodelformentalhealthanalysis AT mdshakilhossain multitaskopinionenhancedhybridbertmodelformentalhealthanalysis AT mfmridha multitaskopinionenhancedhybridbertmodelformentalhealthanalysis AT mejdlsafran multitaskopinionenhancedhybridbertmodelformentalhealthanalysis AT sultanalfarhood multitaskopinionenhancedhybridbertmodelformentalhealthanalysis |