Short Text Topic Modeling Techniques, Applications, and Performance: A Survey
TABLE V: Purity and NMI value of all models on six datasets.
['Model LDA', 'Model  [ITALIC] Purity', 'Biome dicine 0.456±0.011', 'Google News 0.793±0.005', 'Pascal Flickr 0.376±0.013', 'Search Snippets 0.740±0.029', 'Stack Overflow 0.562±0.013', 'Tweet 0.821±0.006', 'Mean Value 0.625±0.013']
We can see that all models outperform long text topic modeling (LDA), except SATM. Here, similar to the conclusions in classification, we can observe that the performance of approaches is highly data set dependent. WNTM achieves the best performance on several datasets but performs poorly on PascalFlickr. GPU-PDMM performs very well on all datasets except SearchSnippets.