Machine Learning and LIME
Building a Sentiment Analysis Predictive Model - Supervised Machine Learning
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.
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