Public articles linked to the same research event.
Journal of Medical Internet Research 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.
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.