Amazon Product Reviews with Appraisal-Based Complexity Labels

A manually annotated dataset of Amazon product reviews labeled for perceived complexity using attitudinal categories from appraisal theory. The dataset likely contains reviews annotated for appreciation (user-friendliness) and judgment (effectiveness). The author is Shoukat Ullah, and the dataset was last updated on May 26, 2026.

Use Cases

• Training hybrid deep learning models for sentiment analysis based on appraisal theory categories.
• Benchmarking transformer and recurrent neural network models on review complexity classification.
• Applying explainable AI methods like SHAP to interpret model predictions for product feedback.
• Analyzing the relationship between product complexity and user perception in e-commerce reviews.

Strengths

• The dataset is manually annotated according to a specific theoretical framework (appraisal theory).
• The associated model achieved a reported mean fold accuracy of 96.13%.
• The dataset is licensed under CC-BY-4.0, facilitating open use.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating a limited scope.

Provenance

Source

Amazon product reviews.

Collection Method

Reviews were manually annotated for perceived complexity using attitudinal categories of appraisal theory.

Freshness

Last updated 2026-05-26 17:43:29; freshness should be verified.