Machine Learning, LIME, and GFI

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This workflow uses a subset of the Kaggle Dataset including 14K customer tweets towards six US airlines (https://www.kaggle.com/crowdflower/twitter-airline-sentiment). Contributors annotated the valence of the tweets as positive, negative or neutral. For this example we use only positive and negative.

If you use this workflow, please cite: Villarroel Ordenes, Francisco, Grant Packard, Davide Proserpio, and Jochen Hartmann, “Using Text Analysis in Service Failure and Recovery: Theory, Workflows, and Models”, Journal of Service Research, Forthcoming.

Nodes

• Column Filter49 ×
• Column Rename39 ×
• RowID33 ×
• CASE Switch Data (Start)30 ×
• Rule Engine Variable30 ×
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Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME H2O Machine Learning Integration
• FeatureKNIME Integrated Deployment
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