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.

Type	Name
	Go back to parent directory
	Building Sentiment Predictor - Deep Learning Based
	Deploying Sentiment Predictor - Deep Learning Based

Machine Learning and Marketing