Mobile App Reviews with Appraisal Theory Sentiment Labels

A dataset of mobile app reviews manually annotated for sentiment based on appraisal theory concepts. The data was created by Ruping Zhang and last updated on June 1, 2026. It is a small dataset of 9.5 KB, stored in an XLS file.

Use Cases

• Training sentiment classifiers based on the appraisal theory labels (High/Low performance).
• Benchmarking lightweight transformer models like MobileBERT for real-time text analysis.
• Studying the application of contrastive learning (SimCLR) for feature extraction in text data.
• Developing explainable AI (XAI) methods for interpreting sentiment model outputs using SHAP.

Strengths

• Manual annotation accuracy was verified using the SHAP method of Explainable AI.
• The proposed model described achieved a mean fold accuracy of 88.63% and ROC AUC of 90.91%.
• Reviews are labeled using a nuanced framework based on appraisal theory (appreciation and judgment).

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 9.5 KB, indicating a limited scope of reviews.

Provenance

Source

Ruping Zhang via figshare.

Collection Method

Mobile app reviews were manually labeled based on performance parameters aligned with appraisal theory.

Freshness

Last updated 2026-06-01 17:52:56; freshness should be verified.