Data Set Creation and Empirical Analysis for Detecting Signs of Depression from Social Media Postings

Sampath, Kayalvizhi; Durairaj, Thenmozhi

doi:10.1007/978-3-031-16364-7_11

Cited by 15 publications

(8 citation statements)

References 25 publications

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“…Kayalvizhi et al 175 A word2vec pre-trained word embedding and random forest classifier achieved their best performance with a 0.877 F 1 score.…”

Section: Author Main Findingsmentioning

confidence: 99%

A systematic review on automated clinical depression diagnosis

Mao,

Wu,

Chen

2023

npj Mental Health Res

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Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

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“…Kayalvizhi et al 175 A word2vec pre-trained word embedding and random forest classifier achieved their best performance with a 0.877 F 1 score.…”

Section: Author Main Findingsmentioning

confidence: 99%

A systematic review on automated clinical depression diagnosis

Mao,

Wu,

Chen

2023

npj Mental Health Res

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“…Feature extraction was based on Glove embeddings and sentiment features for the LSTM and LR respectively and the ensemble classifier achieved a best accuracy and precision of 75.55% and 85.05% respectively. [41] implemented Adaboost, DT, KNN, MLP, LR, NB, SVM, linear discriminant analysis (LDA) and RF for diagnosing depression from social media text on a manually annotated dataset of three classes. Feature extraction was based on TF-IDF, Word2vec and GloVe.…”

Section: Related Workmentioning

confidence: 99%

A Stacking-Based Ensemble Approach with Embeddings from Language Models for Depression Detection from Social Media Text

Gaius,

Mwangi,

Ngunyi

2023

JDAIP

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Depression is a major public health problem around the world and contributes significantly to poor health and poverty. The rate of the number of people being affected is very high compared to the rate of medical treatment of the disease. Thus, the disease often remains untreated and suffering continues. Machine learning has been widely used in many studies in detecting depressive individuals from their contents on online social networks. From the related reviews, it is apparent that the application of stacking for diagnosing depression has been minimal. The study implements stacking based on Extra Tree, Extreme Gradient Boosting, Light Gradient Boosting and Multi-layer perceptron and compares its performance to state of the art bagging and boosting ensemble learners. To better evaluate the effectiveness of the proposed stacking approach, three pretrain word embeddings techniques including: Word2vec, Global Vectors and Embeddings from language models were employed with two datasets. Also, a corrected resampled paired t-test was applied to test the significance of the stacked accuracy against the baseline accuracy. The experimental results shows that the stacking approach yields favourable results with a best accuracy of 99.54%.

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“…However, the nature of depression is diverse, with varying severity levels that encompass minimal, mild, moderate, and severe cases [7]. This has increased the research on novel data collection and approaches that consider the multifaceted nature of depression [30,31]. In recent years, the number of works that aim to capture the varying degrees of intensity in depression is increasing consistently [20,[32][33][34].…”

Section: Related Workmentioning

confidence: 99%

“…For the evaluation phase, we used two publicly available English-language datasets dedicated to the detection of the severity of depression. These datasets, namely, the depression severity dataset (DsD) [30] and the depression signs detection dataset (DepSign) [31], were collected from the Reddit social network. In the following lines, we provide detailed descriptions of these datasets and pertinent statistics drawn from their respective contents.…”

Section: Datamentioning

confidence: 99%

Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations

Muñoz,

Iglesias

2023

Applied Sciences

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Depression is a common and debilitating mental illness affecting millions of individuals, diminishing their quality of life and overall well-being. The increasing prevalence of mental health disorders has underscored the need for innovative approaches to detect and address depression. In this context, text analysis has emerged as a promising avenue. Novel solutions for text-based depression detection commonly rely on deep neural networks or transformer-based models. Although these approaches have yielded impressive results, they often come with inherent limitations, such as substantial computational requirements or a lack of interpretability. This work aims to bridge the gap between substantial performance and practicality in the detection of depression signs within digital content. To this end, we introduce a comprehensive feature framework that integrates linguistic signals, emotional expressions, and cognitive patterns. The combination of this framework with distributional representations contributes to fostering the understanding of language patterns indicative of depression and provides a deeper grasp of contextual nuances. We exploit this combination using traditional machine learning methods in an effort to yield substantial performance without compromising interpretability and computational efficiency. The performance and generalizability of our approach have been assessed through experimentation using multiple publicly available English datasets. The results demonstrate that our method yields throughput on par with more complex and resource-intensive solutions, achieving F1-scores above 70%. This accomplishment is notable, as the proposed method simultaneously preserves the virtues of simplicity, interpretability, and reduced computational overhead. In summary, the findings of this research contribute to the field by offering an accessible and scalable solution for the detection of depression in real-world scenarios.

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Data Set Creation and Empirical Analysis for Detecting Signs of Depression from Social Media Postings

Cited by 15 publications

References 25 publications

A systematic review on automated clinical depression diagnosis

A systematic review on automated clinical depression diagnosis

A Stacking-Based Ensemble Approach with Embeddings from Language Models for Depression Detection from Social Media Text

Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations

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