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Deploying Sentiment Predictor - Deep Learning

Sentiment analysisSentimentDeploymentMarketing AnalyticsDeep learning
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VersionWorkflow created in KAP v5.4Latest, created on 
Mar 25, 2025 11:46 PM
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Deploying a Sentiment Analysis Predictive Model - Deep Learning Approach with an RNN

This workflow applies an RNN, trained on a Kaggle Dataset, on unlabeled social media posts to predict their sentiment. The last component visualizes (1) a bar chart with the number of negative/positive/neutral posts, (2) a word cloud of randomly selected posts, and (3) a table with all collected posts.

This workflow is tailored for Windows. If you run it on another system, you may have to adapt the environment of the Conda Environment Propagation 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.

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Used extensions & nodes

Created with KNIME Analytics Platform version 5.3.3
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    KNIME Base nodesTrusted extension

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    Version 5.3.3

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    KNIME Column Expressions (Labs)Trusted extension

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    Version 5.3.0

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    KNIME Conda IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.3.3

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    KNIME Deep Learning - Keras IntegrationTrusted extension

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    KNIME JavaScript ViewsTrusted extension

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    KNIME JavasnippetTrusted extension

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    KNIME Personal Productivity ToolsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.3.3

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