02_Topic_Scoring_Example
Topic Scoring with Verified Components
The Topic Scorer (Labs) verified component implements an experimental score for semantic coherence, exclusivity and
similarity/distance of topics of one or multiple models.
Read more on the component pages at knime.com/verified-components. References are also available below in this workflow page.
Nodes
- Column Rename77 ×
- Column Filter49 ×
- GroupBy46 ×
- Inject Variables (Data)31 ×
- String Manipulation30 ×
- Show all 96 nodes
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Conda Integration
- FeatureKNIME Data Generation
- FeatureKNIME Distance Matrix
- FeatureKNIME Interactive R Statistics Integration
- Show all 11 modules
Links
- “PoliBlogs08” data set by Eisenstein and Xing 2010dl.acm.org/doi/10.5555/1870658.1870782
- Optimizing semantic coherence in topic models - Mimno et al 2011, Proceedings of the Conference on Empirical Methods in Natural Language Processing 2011dl.acm.org/doi/10.5555/2145432.2145462
- Summarizing topical content with word frequency and exclusivity - Bischof and Airoldi (2012), Proceedings of the 29th International Coference on International Conference on Machine Learningdl.acm.org/doi/10.5555/3042573.3042578
- Topic Scorer (Labs) - KNIME Community Hubhub.knime.com/-/spaces/-/latest/~5_W2h2g6hBY_M0Bc/
- Verified Components project - knime.comwww.knime.com/verified-components