SleepDepNet: Reddit Text Performance Comparison for Sleep and Depression Analysis

A 5.5 KB dataset on figshare, authored by Akshi Kumar and last updated May 7, 2026, under a CC-BY-4.0 license. It contains performance comparison results for the SleepDepNet multi-task learning framework, which models sleep quality and depressive sentiment from user-generated Reddit text. The dataset likely includes experimental results such as F1-scores of 0.89 and 0.86 for the model's classification tasks.

Use Cases

• Benchmarking transformer-based models for sleep quality classification based on the described F1-score performance.
• Evaluating multi-task learning frameworks for joint sentiment and sleep analysis based on the described SleepDepNet architecture.
• Analyzing linguistic patterns linked to emotional states and sleep disturbances based on the described attention mechanisms and topic modeling.
• Developing unified risk assessment metrics like the proposed SleepDepScore based on the integrated model outputs.

Strengths

• Performance metrics are provided, including specific F1-scores of 0.89 and 0.86 for the model's tasks.
• The dataset is openly licensed under CC-BY-4.0.
• The underlying model framework is described in detail, incorporating attention mechanisms and emotion-aware features.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope.
• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The data source is limited to Reddit communities, which may reflect platform-specific biases.

Provenance

Source

Data collected from Reddit communities related to sleep and mental health.

Collection Method

Likely derived from experimental results of the SleepDepNet framework.

Time Range

null

Freshness

Last updated 2026-05-07 17:37:17; freshness should be verified.

Geography

null