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Building Sentiment Predictor - Deep Learning

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

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.

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: fvill
Created at: 2020-11-10
On NodePit since: 2024-12-31
Last update: 2025-01-20
Created with KNIME version: v5.4.0
Tags: sentiment analysissentimentmachine learningsupervised learningRNNKerasDeep Learning

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