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Classification

Pre completed Data Setup Pre-completed steps to set up the data. The CSV reader pulls from this location:knime://knime.workflow/../_INPUT/ClavisExtractUK.xlsxThe flow pulls product reviews from online retailers. The words from the reviews are extracted and manipulated (see inside preprocessing node). The datatable is thenpivoted and grouped together to create a count of each word in each column.A second repitition of the flow comes in below to handle the reviews that are missing the rating. Task 2: Classification Learners, Predictors and Scorers Task 1: Final data prep andpartitioning Step 1: Use a Rule Engine to split the sentiment fromratings into postive and negative.$review_rating$>=4 =>"Pos Sentiment"TRUE =>"Neg Sentiment"Then partition the data 75/25 randomly Linear Logistic Regression Naive Bayes Decision Tree Random Forest Add Sentiment to our Reviews without Ratings Pre completed Data Setup Pre-completed steps to set up the data. The CSV reader pulls from this location:knime://knime.workflow/../_INPUT/ClavisExtractUK.xlsxThe flow pulls product reviews from online retailers. The words from the reviews are extracted and manipulated (see inside preprocessing node). The datatable is thenpivoted and grouped together to create a count of each word in each column.A second repitition of the flow comes in below to handle the reviews that are missing the rating. Task 2: Classification Learners, Predictors and Scorers Task 1: Final data prep andpartitioning Step 1: Use a Rule Engine to split the sentiment fromratings into postive and negative.$review_rating$>=4 =>"Pos Sentiment"TRUE =>"Neg Sentiment"Then partition the data 75/25 randomly Linear Logistic Regression Naive Bayes Decision Tree Random Forest Add Sentiment to our Reviews without Ratings Read Online ReviewsConvert strings toto documentsPreprocessing of documentsNode 301Node 302Node 303Node 305Node 313Make blank tableNode 316Read Online ReviewsConvert strings toto documentsPreprocessing of documentsNode 337Node 338Node 339Node 340Classify into sentimentNode 342Node 343Make blank tableNode 345Node 346Node 347Node 348Node 349Node 350Node 351Node 352Node 353Node 354Node 355Node 356Node 357Node 358Node 359Node 360 Excel Reader (XLS)(deprecated) Strings To Document(deprecated) Preprocessing Joiner (deprecated) Pivot GroupBy Missing Value Missing Value Row Filter Concatenate Prep ReviewsMissing Ratings Excel Reader (XLS)(deprecated) Strings To Document(deprecated) Preprocessing Joiner (deprecated) Pivot GroupBy Missing Value Rule Engine Missing Value Logistic RegressionLearner (deprecated) Row Filter Concatenate Regression Predictor(deprecated) Scorer (deprecated) Partitioning Naive Bayes Learner(deprecated) Scorer (deprecated) Column Filter Naive Bayes Predictor(deprecated) DecisionTree Learner Decision TreePredictor Scorer (deprecated) Random Forest Learner(deprecated) Random Forest Predictor(deprecated) Scorer (deprecated) Random Forest Predictor(deprecated) Column Filter Prep ReviewsMissing Ratings Pre completed Data Setup Pre-completed steps to set up the data. The CSV reader pulls from this location:knime://knime.workflow/../_INPUT/ClavisExtractUK.xlsxThe flow pulls product reviews from online retailers. The words from the reviews are extracted and manipulated (see inside preprocessing node). The datatable is thenpivoted and grouped together to create a count of each word in each column.A second repitition of the flow comes in below to handle the reviews that are missing the rating. Task 2: Classification Learners, Predictors and Scorers Task 1: Final data prep andpartitioning Step 1: Use a Rule Engine to split the sentiment fromratings into postive and negative.$review_rating$>=4 =>"Pos Sentiment"TRUE =>"Neg Sentiment"Then partition the data 75/25 randomly Linear Logistic Regression Naive Bayes Decision Tree Random Forest Add Sentiment to our Reviews without Ratings Pre completed Data Setup Pre-completed steps to set up the data. The CSV reader pulls from this location:knime://knime.workflow/../_INPUT/ClavisExtractUK.xlsxThe flow pulls product reviews from online retailers. The words from the reviews are extracted and manipulated (see inside preprocessing node). The datatable is thenpivoted and grouped together to create a count of each word in each column.A second repitition of the flow comes in below to handle the reviews that are missing the rating. Task 2: Classification Learners, Predictors and Scorers Task 1: Final data prep andpartitioning Step 1: Use a Rule Engine to split the sentiment fromratings into postive and negative.$review_rating$>=4 =>"Pos Sentiment"TRUE =>"Neg Sentiment"Then partition the data 75/25 randomly Linear Logistic Regression Naive Bayes Decision Tree Random Forest Add Sentiment to our Reviews without Ratings Read Online ReviewsConvert strings toto documentsPreprocessing of documentsNode 301Node 302Node 303Node 305Node 313Make blank tableNode 316Read Online ReviewsConvert strings toto documentsPreprocessing of documentsNode 337Node 338Node 339Node 340Classify into sentimentNode 342Node 343Make blank tableNode 345Node 346Node 347Node 348Node 349Node 350Node 351Node 352Node 353Node 354Node 355Node 356Node 357Node 358Node 359Node 360 Excel Reader (XLS)(deprecated) Strings To Document(deprecated) Preprocessing Joiner (deprecated) Pivot GroupBy Missing Value Missing Value Row Filter Concatenate Prep ReviewsMissing Ratings Excel Reader (XLS)(deprecated) Strings To Document(deprecated) Preprocessing Joiner (deprecated) Pivot GroupBy Missing Value Rule Engine Missing Value Logistic RegressionLearner (deprecated) Row Filter Concatenate Regression Predictor(deprecated) Scorer (deprecated) Partitioning Naive Bayes Learner(deprecated) Scorer (deprecated) Column Filter Naive Bayes Predictor(deprecated) DecisionTree Learner Decision TreePredictor Scorer (deprecated) Random Forest Learner(deprecated) Random Forest Predictor(deprecated) Scorer (deprecated) Random Forest Predictor(deprecated) Column Filter Prep ReviewsMissing Ratings

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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: richardson.bg
Created at: 2017-05-15
On NodePit since: 2024-10-14
Last update: 2024-10-18
Created with KNIME version: v5.2.0

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