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03_​Sentiment_​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 sentimentlabelColor by sentimentlabelTraining / test setTransformation of strings to documentsPreprocessing of documentsCreate bit vectorsfor documentsRead IMDb reviewsfrom CSV fileNode 362Node 363Node 364Category To Class Color Manager Partitioning Decision TreePredictor Document Creation Preprocessing DecisionTree Learner Document Vector CSV Reader XGBoost Predictor Column Appender Column Filter Scorer Scorer ROC Curve XGBoost TreeEnsemble Learner Naive Bayes Learner Naive BayesPredictor Scorer Extract sentimentlabelColor by sentimentlabelTraining / test setTransformation of strings to documentsPreprocessing of documentsCreate bit vectorsfor documentsRead IMDb reviewsfrom CSV fileNode 362Node 363Node 364Category To Class Color Manager Partitioning Decision TreePredictor Document Creation Preprocessing DecisionTree Learner Document Vector CSV Reader XGBoost Predictor Column Appender Column Filter Scorer Scorer ROC Curve XGBoost TreeEnsemble Learner Naive Bayes Learner Naive BayesPredictor Scorer

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

Download Workflow
Created by: Kilian
Created at: 2014-11-08
On NodePit since: 2023-02-26
Last update: 2024-11-21
Created with KNIME version: v4.7.1
Tags: sentiment analysisNLPNatural Language Processingtext classificationPracticing Data Science

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