02_​Topic_​Scoring_​Example

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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 2010

  dl.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 2011

  dl.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 Learning

  dl.acm.org/doi/10.5555/3042573.3042578
• Topic Scorer (Labs) - KNIME Community Hub

  hub.knime.com/-/spaces/-/latest/~5_W2h2g6hBY_M0Bc/
• Verified Components project - knime.com

  www.knime.com/verified-components