Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content

• URL: http://arxiv.org/abs/2311.05075v1
• Date: Thu, 9 Nov 2023 00:15:06 GMT
• Title: Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content
• Authors: Haijian Shao, Ming Zhu, Shengjie Zhai
• Abstract summary: We propose a novel semantic feature preprocessing technique with a three-folded structure. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. Our methods, when compared to seven benchmark models, demonstrate significant performance improvements.
• Score: 3.6195994708545016
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Amid growing global mental health concerns, particularly among vulnerable groups, natural language processing offers a tremendous potential for early detection and intervention of people's mental disorders via analyzing their postings and discussions on social media platforms. However, ultra-sparse training data, often due to vast vocabularies and low-frequency words, hinders the analysis accuracy. Multi-labeling and Co-occurrences of symptoms may also blur the boundaries in distinguishing similar/co-related disorders. To address these issues, we propose a novel semantic feature preprocessing technique with a three-folded structure: 1) mitigating the feature sparsity with a weak classifier, 2) adaptive feature dimension with modulus loops, and 3) deep-mining and extending features among the contexts. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. We utilize the Reddit Mental Health Dataset 2022 to examine conditions such as Anxiety, Borderline Personality Disorder (BPD), and Bipolar-Disorder (BD) and present solutions to the data sparsity challenge, highlighted by 99.81% non-zero elements. After applying our preprocessing technique, the feature sparsity decreases to 85.4%. Overall, our methods, when compared to seven benchmark models, demonstrate significant performance improvements: 8.0% in accuracy, 0.069 in precision, 0.093 in recall, 0.102 in F1 score, and 0.059 in AUC. This research provides foundational insights for mental health prediction and monitoring, providing innovative solutions to navigate challenges associated with ultra-sparse data feature and intricate multi-label classification in the domain of mental health analysis.

Related papers

• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [45.630166504856255]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• 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)
• A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires [0.2580765958706853]
  We propose a novel approach that captures the semantic meanings directly from the text and compares them to symptom-related descriptions. Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.
  arXiv  Detail & Related papers  (2023-06-05T15:23:55Z)
• Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media [27.799032561722893]
  We report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.
  arXiv  Detail & Related papers  (2022-12-19T20:31:47Z)
• 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)
• Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation [75.3147962600095]
  We propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set.
  arXiv  Detail & Related papers  (2020-10-30T18:45:16Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.