Deploying Sentiment Predictor - Deep Learning

Deploying a Sentiment Analysis Predictor - Deep Learning using an RNN-LSTM

This workflow applies an RNN-LSTMs, trained on the Kaggle Dataset (https://www.kaggle.com/crowdflower/twitter-airline-sentiment), on a new set of unlabeled tweets around different airlines to predict customers' sentiment. The last component visualizes (1) the bar chart with the number of negative/positive/neutral tweets, (2) the word cloud of all collected tweets, and (3) the table with all collected tweets.

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.