Summary of Emoscan: Automatic Screening Of Depression Symptoms in Romanized Sinhala Tweets, by Jayathi Hewapathirana and Deshan Sumanathilaka
EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets
by Jayathi Hewapathirana, Deshan Sumanathilaka
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed machine learning-based framework uses Romanized Sinhala social media data to identify individuals at risk of depression by analyzing language patterns, sentiment, and behavioral cues. The study compares Neural Networks with classical machine learning techniques, showing that the Neural Network with an attention layer achieves 93.25% accuracy in detecting depression symptoms, outperforming current state-of-the-art methods. This research contributes to proactive interventions and support systems for mental health, influencing both research and practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses social media data to find people who might be depressed. They developed a special machine learning model that looks at what people say online, how they feel about it, and what they do on their accounts. This model is better than old methods for finding people who might need help with depression. It’s like having a smart tool that can tell when someone needs support. Mental health experts, government officials, and social media companies can use this information to make a difference. |
Keywords
* Artificial intelligence * Attention * Machine learning * Neural network
