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Building a Sentiment Analysis Predictive Model - Deep Learning using an RNN

Sentiment analysis Sentiment Machine learning Supervised learning RNN
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This workflow uses a 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 and neutral. Once users are satisfied with the model evaluation, they should export (1) the Dictionary, (2) the Category to Number Model, and (3) the Trained Network for deployment in non-annotated data. This workflow is tailored for Windows. If you run it on another system, you may have to (1) adapt the environment of the Conda Environment Propagation node and (2) make sure that the Keras Embedding Layer node has the right number of units, which depends on the native encoding of the system and is indicated in the CSV Reader node. If you use this workflow, please cite: 
F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.1
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    KNIME Base nodes Trusted extension

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    KNIME Deep Learning - Keras Integration Trusted extension

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    KNIME Expressions Trusted extension

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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

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