EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets

• URL: http://arxiv.org/abs/2403.19728v1
• Date: Thu, 28 Mar 2024 10:31:09 GMT
• Title: EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets
• Authors: Jayathi Hewapathirana, Deshan Sumanathilaka,
• Abstract summary: This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioural cues.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioural cues within a comprehensive dataset of social media posts. The research has been carried out to compare the suitability of Neural Networks over the classical machine learning techniques. The proposed Neural Network with an attention layer which is capable of handling long sequence data, attains a remarkable accuracy of 93.25% in detecting depression symptoms, surpassing current state-of-the-art methods. These findings underscore the efficacy of this approach in pinpointing individuals in need of proactive interventions and support. Mental health professionals, policymakers, and social media companies can gain valuable insights through the proposed model. Leveraging natural language processing techniques and machine learning algorithms, this work offers a promising pathway for mental health screening in the digital era. By harnessing the potential of social media data, the framework introduces a proactive method for recognizing and assisting individuals at risk of depression. In conclusion, this research contributes to the advancement of proactive interventions and support systems for mental health, thereby influencing both research and practical applications in the field.

Related papers

• Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media [5.570905441172371]
  Existing works about early depression detection on social media lacked interpretability. We develop a novel method that leverages a Heterogeneous Subgraph Network with Prompt Learning. Our proposed method significantly outperforms state-of-the-art methods for depression detection on social media.
  arXiv  Detail & Related papers  (2024-07-12T06:20:59Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks [0.0]
  This study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques. New models outperform the previous approach with an accuracy rate of up to 97%.
  arXiv  Detail & Related papers  (2023-06-29T12:25:19Z)
• DEPAC: a Corpus for Depression and Anxiety Detection from Speech [3.2154432166999465]
  We introduce a novel mental distress analysis audio dataset DEPAC, labeled based on established thresholds on depression and anxiety screening tools. This large dataset comprises multiple speech tasks per individual, as well as relevant demographic information. We present a feature set consisting of hand-curated acoustic and linguistic features, which were found effective in identifying signs of mental illnesses in human speech.
  arXiv  Detail & Related papers  (2023-06-20T12:21:06Z)
• Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology [0.0]
  Current technologies are an exciting opportunity to improve behavioral characterization. New capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometers, open avenues of novel questioning. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge.
  arXiv  Detail & Related papers  (2023-05-24T17:45:10Z)
• Predicting mental health using social media: A roadmap for future development [0.0]
  Mental disorders such as depression and suicidal ideation affect more than 300 million people over the world. On social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques.
  arXiv  Detail & Related papers  (2023-01-25T08:08:29Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.