Model Performance Summary for Cancer Support Text Emotional Tone Classification

Shuo Xu's dataset, last updated May 29, 2026, summarizes test performance for five model families classifying emotional tone in cancer peer-support text. The 5.5 KB Excel file contains results from a study using the 'Mental Health Insights: Vulnerable Cancer Survivors & Caregivers' dataset, comparing models like TF-IDF Logistic Regression, Random Forest, LightGBM, GRU, and fine-tuned ALBERT. It includes performance metrics such as weighted F1 and macro one-vs-rest AUC with bootstrap confidence intervals.

Use Cases

• Benchmarking NLP model performance on emotional tone classification based on the described comparison of five model families.
• Studying the impact of LLM-based annotation on label distribution based on the described shift in label prevalence.
• Evaluating token augmentation techniques for incorporating context features based on the described prepending of LLM-extracted variables.
• Analyzing error patterns in multi-class sentiment classification based on the described polarity-reversing and adjacent errors.

Strengths

• Performance metrics include weighted F1 and macro one-vs-rest AUC with bootstrap confidence intervals, providing statistical rigor.
• Model comparison is based on a 60/20/20 stratified train/validation/test split with hyperparameters selected on validation data only.
• The study introduces two methodological extensions: LLM-based annotation and token-based augmentation with structured variables.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating it is a summary of results rather than a primary data corpus.

Provenance

Source

figshare, author Shuo Xu

Collection Method

Derived from a study using the 'Mental Health Insights: Vulnerable Cancer Survivors & Caregivers' dataset; models were trained and tested on a stratified split.

Freshness

Last updated 2026-05-29 17:34:09; freshness should be verified.