DepressionEmo: A novel dataset for multilabel classification of depression emotions
Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and...
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2024
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oai:scholar.dlu.edu.vn:123456789-35522024-09-06T08:19:07Z DepressionEmo: A novel dataset for multilabel classification of depression emotions Rahman, Abu Bakar Siddiqur Tạ, Hoàng Thắng Najjar, Lotfollah Azadmanesh, Azad Gönul, Ali Saffet Dataset; Depression identification; Emotion analysis; Psycholinguistic analysis; Text classification Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and impact on individuals. In this paper, we introduce a novel dataset named DepressionEmo designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models and validating the quality by annotators and ChatGPT, exhibiting an acceptable level of inter-rater reliability between annotators. The correlation between emotions, and linguistic analysis are conducted on DepressionEmo. Besides, we provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and LightGBM; and deep learning methods such as BERT, BART, GAN-BERT, and T5. Despite achieving the same F1 Macro score of 0.76 as BART, the pretrained BERT model, bert-base-uncased, stands out as the most efficient model in our experiments due to its lower number of parameters. Across all emotions, the highest F1 Macro value is achieved by suicide intent, indicating a certain value of our dataset in identifying emotions in individuals with depression symptoms through text analysis. The curated dataset is publicly available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo. Khoa Công nghệ Thông tin 5 Tạ Hoàng Thắng JOURNAL OF AFFECTIVE DISORDERS, dòng 1057, trang 30/57 2024-09-04T01:46:48Z 2024-09-04T01:46:48Z 2024-08-28 Journal article Bài báo đăng trên tạp chí thuộc ISI, bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/3552 10.1016/j.jad.2024.08.013 en Journal of affective disorders |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Dataset; Depression identification; Emotion analysis; Psycholinguistic analysis; Text classification |
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Dataset; Depression identification; Emotion analysis; Psycholinguistic analysis; Text classification Rahman, Abu Bakar Siddiqur Tạ, Hoàng Thắng Najjar, Lotfollah Azadmanesh, Azad Gönul, Ali Saffet DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| description |
Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and impact on individuals. In this paper, we introduce a novel dataset named DepressionEmo designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models and validating the quality by annotators and ChatGPT, exhibiting an acceptable level of inter-rater reliability between annotators. The correlation between emotions, and linguistic analysis are conducted on DepressionEmo. Besides, we provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and LightGBM; and deep learning methods such as BERT, BART, GAN-BERT, and T5. Despite achieving the same F1 Macro score of 0.76 as BART, the pretrained BERT model, bert-base-uncased, stands out as the most efficient model in our experiments due to its lower number of parameters. Across all emotions, the highest F1 Macro value is achieved by suicide intent, indicating a certain value of our dataset in identifying emotions in individuals with depression symptoms through text analysis. The curated dataset is publicly available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo. |
| format |
Journal article |
| author |
Rahman, Abu Bakar Siddiqur Tạ, Hoàng Thắng Najjar, Lotfollah Azadmanesh, Azad Gönul, Ali Saffet |
| author_facet |
Rahman, Abu Bakar Siddiqur Tạ, Hoàng Thắng Najjar, Lotfollah Azadmanesh, Azad Gönul, Ali Saffet |
| author_sort |
Rahman, Abu Bakar Siddiqur |
| title |
DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| title_short |
DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| title_full |
DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| title_fullStr |
DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| title_full_unstemmed |
DepressionEmo: A novel dataset for multilabel classification of depression emotions |
| title_sort |
depressionemo: a novel dataset for multilabel classification of depression emotions |
| publishDate |
2024 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/3552 |
| _version_ |
1813142630541819904 |