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Emotion_​Classification

Sentiment Analysis (Classification) of Documents

This workflow shows how to import text from a csv file, convert it to documents, preprocess the documents and transform them into numerical document vectors. Finally two predictive models are trained on the vectors to predict the sentiment class of the documents.





Extract EmotionlabelColor by EmotionlabelTraining / test setTransformation of strings to documentsPreprocessing of documentsCreate bit vectorsfor documentsNode 361Node 362Node 363Node 364Category To Class Color Manager Partitioning Document Creation Preprocessing Document Vector Excel Reader Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Scorer Extract EmotionlabelColor by EmotionlabelTraining / test setTransformation of strings to documentsPreprocessing of documentsCreate bit vectorsfor documentsNode 361Node 362Node 363Node 364Category To Class Color Manager Partitioning Document Creation Preprocessing Document Vector Excel Reader Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Scorer

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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: Kilian
Created at: 2014-11-08
On NodePit since: 2022-09-13
Last update: 2024-11-21
Created with KNIME version: v4.6.1
Tags: sentiment analysisNLPNatural Language Processingtext classificationPracticing Data Science

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