Mental Health Corpus

Enhanced Dataset Overview

The expanded version could include more detailed classifications like Supportive, Neutral, and Harmful to reflect a wider spectrum of mental health-related discourse. This offers a nuanced analysis for various use cases, such as:

1. Sentiment Analysis: This aspect of the dataset enables researchers to understand the emotional tone of discussions related to mental health. By identifying positive or negative sentiments, it becomes easier to track how language shifts in response to mental health conditions or treatment approaches. Expanding sentiment categories (such as “Hopeful,” “Anxious,” “Despairing”) can add depth to the analysis.
2. Toxicity and Language Harm: Toxicity detection is critical in mental health forums where harmful language may have a significant impact on users’ well-being. Labels indicating toxicity help monitor, filter, and reduce the spread of damaging content. The expanded dataset could further classify types of toxicity, such as Aggressive, Dismissive, or Stigmatizing, offering a more comprehensive view of harmful interactions.
3. Mental Health Language Analysis: Mental health discourse analysis benefits from an expanded corpus with metadata such as Time of Posting, Demographics, or even Response Triggers to provide researchers with insights into how specific mental health topics are discussed, and whether certain linguistic patterns are linked to specific emotional or cognitive states.

Future Scope

To enhance the dataset further, additional elements like Emotional Context and Supportive Commentary Tags can be included. This would enable models to distinguish between neutral comments and those that are actively supportive or harmful.