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Deploying Sentiment Predictor - Deep Learning

Deploying a Sentiment Analysis Predictive Model - Deep Learning using an Recurrent Neural Network (RNN)

This workflow applies an RNN, trained on the Kaggle Dataset (https://www.kaggle.com/crowdflower/twitter-airline-sentiment), on new tweets around #xxx to predict their 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.




1. Collect and processtwitter data. Users couldalso collect data from morebrands simultaneusly andthen concatenate tables. 2. Prepare and classify new data. Perform an index encoding using the dictionarycreated in the training workflow and apply the trained network. This blog postdescribes different encoding options https://www.knime.com/blog/text-encoding-a-review 3. Visualize data - bar chart of # positive, negative, neutraltweets - word cloud - table with tweets Deploying a Sentiment Analysis Predictive Model - Deep Learning using an Recurrent Neural Network (RNN) This workflow applies an RNN, trained on the Kaggle Dataset (https://www.kaggle.com/crowdflower/twitter-airline-sentiment), on new tweets around #xxx to predict theirsentiment. 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 tablewith 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. Class withhighest probabilitySet up a conda environmentRead trainednetworkCategory encodingRead dictionaryVisualization Index encoding basedon dictionary Extract Prediction Conda EnvironmentPropagation Keras NetworkReader PMML Reader Tweet Extraction Keras NetworkExecutor Table Reader Joiner 1. Collect and processtwitter data. Users couldalso collect data from morebrands simultaneusly andthen concatenate tables. 2. Prepare and classify new data. Perform an index encoding using the dictionarycreated in the training workflow and apply the trained network. This blog postdescribes different encoding options https://www.knime.com/blog/text-encoding-a-review 3. Visualize data - bar chart of # positive, negative, neutraltweets - word cloud - table with tweets Deploying a Sentiment Analysis Predictive Model - Deep Learning using an Recurrent Neural Network (RNN) This workflow applies an RNN, trained on the Kaggle Dataset (https://www.kaggle.com/crowdflower/twitter-airline-sentiment), on new tweets around #xxx to predict theirsentiment. 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 tablewith 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. Class withhighest probabilitySet up a conda environmentRead trainednetworkCategory encodingRead dictionaryVisualization Index encoding basedon dictionary Extract Prediction Conda EnvironmentPropagation Keras NetworkReader PMML Reader Tweet Extraction Keras NetworkExecutor Table Reader Joiner

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

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Created by: fvill
Created at: 2021-04-12
On NodePit since: 2023-07-06
Last update: 2026-01-07
Created with KNIME version: v5.0.0
Tags: sentiment analysissentimentdeploymentMarketing Analyticsdeep learningRNN

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