A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis
This study created a controlled synthetic benchmark for educational aspect-based sentiment analysis using 10,000 fake course reviews across 20 pedagogical aspects. It details a generation protocol where targets and context are sampled separately to create realistic data. The benchmark allows researchers to compare different AI models and methods in this specific domain, proving synthetic supervision is useful for studying ABSA.