Summary of Reliability Analysis Of Psychological Concept Extraction and Classification in User-penned Text, by Muskan Garg et al.
Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text
by Muskan Garg, MSVPJ Sathvik, Amrit Chadha, Shaina Raza, Sunghwan Sohn
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 recent surge in computational advancements for mental health analysis has led to the development of responsible AI models that can quantify psychological concepts from user-generated texts on social media. This paper advances existing binary classification datasets by moving towards a higher-level task of reliability analysis through explanations, serving as a safety measure. The LoST dataset is annotated to capture nuanced textual cues suggesting low self-esteem in Reddit posts, with focus on three types of cues: trigger words, text indicators emphasizing low self-esteem, and words describing consequences. Pre-trained language models are used to examine the attention mechanism for a domain-specific psychology-grounded task, highlighting the need for shifting focus from triggers and consequences to comprehensive explanations that emphasize LoST indicators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to analyze social media posts to understand people’s mental health. It wants to make sure these computers don’t just say something is wrong without explaining why. The researchers created a special dataset to help them do this, by looking at words and phrases that might mean someone has low self-esteem. They also used special language models to figure out what parts of the posts are most important for understanding mental health. This can help us make computers that are more helpful and less scary. |
Keywords
» Artificial intelligence » Attention » Classification
