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Deploying Sentiment Predictor

Deploying a Sentiment Analysis Predictive Model - Supervised Machine Learning

This workflow applies an SVM model, trained on the Kaggle Dataset (https://www.kaggle.com/crowdflower/twitter-airline-sentiment), to predict sentiment on new tweets around the query "to:AmericanAir." This query retrieves tweets directed to American Airlines. 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. Selecting a word in the word cloud fetches its corresponding tweets.

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:

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Created by: fvill
Created at: 2021-04-12
On NodePit since: 2025-03-26
Last update: 2025-03-31
Created with KNIME version: v5.3.2
Tags: sentiment analysissentimentmachine learningSVMdeploymentMarketing AnalyticsTwittertweets

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