National Tsing Hua University

Using 5,467 Google Reviews posted in 2024 from all 24 medical centers in Taiwan, this study compared the common LDA dominant-topic assignment with GPT-based aspect-based sentiment analysis (ABSA) on the same corpus, finding that aspect-bearing reviews discussed an average of 2.05 distinct service aspects, that dominant-topic assignment yielded an illustrative 51.2% representational compression, that a soft-assignment LDA baseline reduced count-level compression to 1.7% but left semantic alignment limited (mean set Jaccard=0.33) and carried no aspect-level sentiment polarity, that 11.0% of multiaspect reviews showed cross-aspect mixed sentiment with Technical-Functional Divergence accounting for 61.

Using 5,467 Google Reviews posted in 2024 from all 24 medical centers in Taiwan, this study compared the common LDA dominant-topic assignment with GPT-based aspect-based sentiment analysis (ABSA) on the same corpus, finding that aspect-bearing reviews discussed an average of 2.05 distinct service aspects, that dominant-topic assignment yielded an illustrative 51.2% representational compression, that a soft-assignment LDA baseline reduced count-level compression to 1.7% but left semantic alignment limited (mean set Jaccard=0.33) and carried no aspect-level sentiment polarity, that 11.0% of multiaspect reviews showed cross-aspect mixed sentiment with Technical-Functional Divergence accounting for 61.