Depression Analysis from Social Media Data in Bangla Language: An Ensemble Approach

Mohammed, Marian Binte; Abir, Abu Saleh Md.; Salsabil, Lubaba; Shahriar, Mahir; Fahmin, Ahmed

doi:10.1109/etcce54784.2021.9689887

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Mumu et al [10] used the hybrid CNN-LSTM model to successfully identify a depressive text with an accuracy of 81.05%. Mohammad et al [11] employed an Extra Tree classifier for feature extraction and utilized the Principal Component Analysis technique to minimize feature dimensionality. Afterward, the authors applied the eXtreme Gradient Boost classifier, showing the highest 92.80% accuracy and 93.61% F1-score.…”

Section: Depression Detection From the 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 Bengali Contentmentioning

confidence: 99%

Untitled

2024

J. Eng. Adv.

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“…Usando a técnica de ensemble learning, sete artigos utilizam essa abordagem para criar o modelo de classificac ¸ão. Além do último que discutimos, também apresentamos os trabalhos de [Mohammed et al 2021], que investigam um modelo diferente sobre texto que não seja em inglês.…”

Section: Estado Da Arteunclassified

Identificação de Sintomas de Depressão por Dados de Mídias Sociais: Aplicando Design Science Research para Desenvolver um Modelo de Classificação

Lima Filho,

Silva,

Oliveira

2024

Anais Estendidos Do XX Simpósio Brasileiro De Sistemas De Informação (SBSI 2024)

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O artigo aborda a prevalência da depressão como uma condição incapacitante e destaca a importância da identificação precoce de sintomas para intervenções oportunas. Explorando o conteúdo gerado pelo usuário em redes sociais, o estudo propõe o uso de modelos de aprendizado de máquina na detecção de sintomas depressivos. Seguindo a metodologia DSR, a pesquisa valida a eficácia desses modelos em comparação com abordagens existentes, envolvendo profissionais de saúde e especialistas do domínio. O artigo introduz um modelo de stacking inovador, utilizando métricas LIWC de posts em redes sociais, contribuindo para a compreensão de soluções baseadas em aprendizado de máquina na identificação de sintomas do transtorno depressivo.

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“…However, they have found 54.45% accuracy for SVM and 58.87% accuracy for BiLSTM. In [12] the authors have analyzed social media depression related posts for a binary classification system. The preprocessing steps consisted of removing punctuations and stopwords and created a balanced dataset.…”

Section: Related Workmentioning

confidence: 99%

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

Paul,

Ahmed,

Hasan

et al. 2023

INDJCSE

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Depression is a psychological phenomenon that affects the metal health of people. At the maximum level of depression, one may commit suicide. Besides, it hampers the family and social activities. As a result, identifying depressed persons is critical before proceeding with physiological treatment. Social media has become the most powerful platform for expressing feelings of individuals and also became a potential source of depressive data. Some handy research works are available for English data while it is poor for Bengali data. We have created a noble dataset in Bengali and applied different machine learning and deep learning methods for performance evaluation. A combined method of CNN and GRU gives us the highest accuracy of 87.11%. Our research finds that the performance of CNN+GRU is better compared to other previous research works.

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Depression Analysis from Social Media Data in Bangla Language: An Ensemble Approach

Cited by 7 publications

References 15 publications

Untitled

Untitled

Identificação de Sintomas de Depressão por Dados de Mídias Sociais: Aplicando Design Science Research para Desenvolver um Modelo de Classificação

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

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