ArticleMultimedia tools and applications2022
An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- PoseShot: hybrid CNN-BiLSTM transformer model for free throw action recognition via pose analysis.Scientific reports · 2026Article
- DCEM-TCRCN: an innovative approach to depression detection using wearable IoT devices and deep learning.International journal of computer assisted radiology and surgery · 2025Article
- Analyzing Depression in College Students Using NLP and Transformer Models: Implications for Career and Educational Counseling.Brain and behavior · 2025Article
- A novel and efficient personalized stress detection technique using a deep learning model.Scientific reports · 2025Article
- Mining Suicidal Ideation in Chinese Social Media: A Dual-Channel Deep Learning Model with Information Gain Optimization.Entropy (Basel, Switzerland) · 2025Article
- Article
- Feature-based enhanced boosting algorithm for depression detection.PeerJ. Computer science · 2025Article
- Psychological disorder detection: A multimodal approach using a transformer-based hybrid model.MethodsX · 2024Article
- Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks.Scientific reports · 2024Article
- Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.Scientific reports · 2024Article
- Article
- AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.Neural processing letters · 2022Article
- Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.Sensors (Basel, Switzerland) · 2022Article
- Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset.Bioengineering (Basel, Switzerland) · 2022Article
- Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm.Frontiers in public health · 2022Article
- An optimized deep learning approach for suicide detection through Arabic tweets.PeerJ. Computer science · 2022Article
- Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.Computational intelligence and neuroscience · 2022Article
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2 authors.
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Abstract
Depression has become one of the most widespread mental health disorders across the globe. Depression is a state of mind which affects how we think, feel, and act. The number of suicides caused by depression has been on the rise for the last several years. This issue needs to be addressed. Considering the rapid growth of various social media platforms and their effect on society and the psychological context of a being, it's becoming a platform for depressed people to convey feelings and emotions, and to study their behavior by mining their social activity through social media posts. The key objective of our study is to explore the possibility of predicting a user's mental condition by classifying the depressive from non-depressive ones using Twitter data. Using textual content of the user's tweet, semantic context in the textual narratives is analyzed by utilizing deep learning models. The proposed model, however, is a hybrid of two deep learning architectures, Convolutional Neural Network (CNN) and bi-directional Long Short-Term Memory (biLSTM) that after optimization obtains an accuracy of 94.28% on benchmark depression dataset containing tweets. CNN-biLSTM model is compared with Recurrent Neural Network (RNN) and CNN model and also with the baseline approaches. Experimental results based on various performance metrics indicate that our model helps to improve predictive performance. To examine the problem more deeply, statistical techniques and visualization approaches were used to show the profound difference between the linguistic representation of depressive and non-depressive content.
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