Summary of A Domain-agnostic Neurosymbolic Approach For Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media During Covid-19, by Vedant Khandelwal and Manas Gaur and Ugur Kursuncu and Valerie Shalin and Amit Sheth
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
by Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit Sheth
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces a novel approach to monitoring public sentiment on social media during health crises like COVID-19. Traditional frequency-based methods can miss new content due to evolving language, but by integrating neural networks with symbolic knowledge sources like lexicons and slang term dictionaries, the authors enhance the detection and interpretation of mental health-related tweets relevant to COVID-19. The method is evaluated using large datasets from social media, subreddits, and news articles, outperforming purely data-driven models with an F1 score exceeding 92%. This approach also shows faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary During the COVID-19 pandemic, it’s important to monitor public sentiment on social media. Traditional methods can miss new information because language is constantly changing. The authors created a new way to analyze tweets that combines AI with human-curated knowledge sources like dictionaries and slang term lists. This helps detect and understand mental health-related tweets about COVID-19 better than traditional methods. The approach was tested using many datasets from social media, online forums, and news articles. |
Keywords
» Artificial intelligence » F1 score » Fine tuning
