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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 a predictive model is trained on the vectors to predict the sentiment class of the documents.


The workflow reads IMDb reviews, pre-processes the documents and trains a model to predict the sentiment of movie reviews Data Import and Preprocessing Predictive Modeling and Scoring Extract sentimentlabelColor by sentimentlabelTraining / test setApply decisiontree modelTransformation of strings to documentsPreprocessing of documentsBuild predictive modelCreate bit vectorsfor documentsScore decisiontree modelScore decisiontree modelRead IMDb reviewsfrom CSV file Category To Class Color Manager Partitioning Decision TreePredictor Document Creation Preprocessing DecisionTree Learner Document Vector Scorer ROC Curve CSV Reader The workflow reads IMDb reviews, pre-processes the documents and trains a model to predict the sentiment of movie reviews Data Import and Preprocessing Predictive Modeling and Scoring Extract sentimentlabelColor by sentimentlabelTraining / test setApply decisiontree modelTransformation of strings to documentsPreprocessing of documentsBuild predictive modelCreate bit vectorsfor documentsScore decisiontree modelScore decisiontree modelRead IMDb reviewsfrom CSV file Category To Class Color Manager Partitioning Decision TreePredictor Document Creation Preprocessing DecisionTree Learner Document Vector Scorer ROC Curve CSV Reader

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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: 2021-03-06
Last update: 2024-11-21
Created with KNIME version: v4.3.1
Tags: sentiment analysisNLPNatural Language Processingtext classification

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