Analyzing Depression on Social Media Utilizing Machine Learning and Deep Learning Methods

Paul, Pintu Chandra; Ahmed, Md. Tofael; Hasan, Md. Rakib; Rajee, Alimul; Sultana, Kashmi

doi:10.21817/indjcse/2023/v14i5/231405049

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…They obtained the highest precision of 83.60%, with a sensitivity of 87.10% and a specificity of 79.30%. Paul et al [23] proposed a binary classification model for analyzing depressive data. The authors applied different ML and DL methods, in which the combined method of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) with the Bag-of-Words feature extraction technique performed the best accuracy score at 87.11%.…”

Section: Depression Detection From the Not Bengali Contentmentioning

confidence: 99%

Untitled

2024

J. Eng. Adv.

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Depression is an ordinary mental health-related disorder that hampers people's daily activities, and sometimes, it destroys an individual's life. It is one of the major social issues at present. Since depressed people use various social networking sites for sharing their thoughts and feelings, many scholars have tried to identify depression texts in highly resourced languages like English; however, only a small quantity of papers are detected in the resource-constrained Bengali language. This paper focuses on developing a depression intensity detection system from Bengali text data. In this regard, this study experiments on a 2,596 sample-sized dataset with four levels of depression by utilizing five state-of-the-art transformer models, including multilingual Bidirectional Encoder Representations from Transformers, DistilmBERT, XLM-RoBERTa, Bangla-BERT-Base, and BanglaBERT, and suggests a new ensemble method called MaxOfAvgProb. This method goes beyond the performance of the previous work on the same dataset, scoring 63.47% F1-score and 62.90% accuracy. To increase the reliability of the proposed method, we utilize this approach on another available dataset with 4,897 entries. In this case, our recommended method also surpasses the performance of the existing work on the same dataset, with accuracy at 86.45% and F1-score at 86.35%. Identifying the intensity of depression, depressed people may get proper counseling or treatment from their respected guardians or psychologists according to the victims' level of depression.

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Section: Depression Detection From the Not Bengali Contentmentioning

confidence: 99%

Untitled

2024

J. Eng. Adv.

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Depression Intensity Identification using Transformer Ensemble Technique for the Resource-constrained Bengali Language

Hoque,

Salma,

Uddin

et al. 2024

J. Eng. Adv.

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Depression is an ordinary mental health-related disorder that hampers people’s daily activities, and sometimes, it destroys an individual’s life. It is one of the major social issues at present. Since depressed people use various social networking sites for sharing their thoughts and feelings, many scholars have tried to identify depression texts in highly resourced languages like English; however, only a small quantity of papers are detected in the resource-constrained Bengali language. This paper focuses on developing a depression intensity detection system from Bengali text data. In this regard, this study experiments on a 2,596 sample-sized dataset with four levels of depression by utilizing five state-of-the-art transformer models, including multilingual Bidirectional Encoder Representations from Transformers, DistilmBERT, XLM-RoBERTa, Bangla-BERT-Base, and BanglaBERT, and suggests a new ensemble method called MaxOfAvgProb. This method goes beyond the performance of the previous work on the same dataset, scoring 63.47% F1-score and 62.90% accuracy. To increase the reliability of the proposed method, we utilize this approach on another available dataset with 4,897 entries. In this case, our recommended method also surpasses the performance of the existing work on the same dataset, with accuracy at 86.45% and F1-score at 86.35%. Identifying the intensity of depression, depressed people may get proper counseling or treatment from their respected guardians or psychologists according to the victims’ level of depression.

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Artificial Intelligence Technologies in Mental Health

Nurfadhilah,

Yoganingrum,

Latief

et al. 2025

Advances in Computational Intelligence and Robotics

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This chapter explores the incorporation of artificial intelligence (AI) into mental health care, with a particular focus on managing depression. AI has significantly enhanced the promotion, detection, diagnosis, treatment, and monitoring of depression by leveraging technologies such as machine learning, natural language processing, and wearable devices. This chapter also discusses various AI-driven approaches, including the analysis of questionnaires, medical records, social media, speech data, electroencephalogram, magnetic resonance imaging, chatbots, virtual reality, face analysis, robots, multimodal methods, and wearable devices. Each of these technologies offers unique benefits, such as increased accuracy in detecting depression, personalized treatment plans, and continuous patient monitoring. However, the challenges linked to AI in mental health, such as data privacy issues, biases in algorithms, and the complexity of human emotions. The chapter concludes by highlighting the opportunities and future research directions and innovation for AI in enhancing of depression care.

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Analyzing Depression on Social Media Utilizing Machine Learning and Deep Learning Methods

Cited by 3 publications

References 11 publications

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Depression Intensity Identification using Transformer Ensemble Technique for the Resource-constrained Bengali Language

Artificial Intelligence Technologies in Mental Health

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