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IMDB_​Sentiment_​With_​Viz

Workflow

IMDB_Sentiment_With_Viz
Text ProcessingIMDB
Part 2:Enrichment- Use the POSTagger node toassign part ofspeech tagsOptional: Use theDocument Viewernode to visualize thetags Part 3: Preprocessing- Use the Punctuation Erasure node to remove punctuation- Use the Number Filter node to remove numbers- Use the N Chars Filter node to remove words with less than 3 characters- Use the Stop Word Filter node to delete words with very little meaning, such as "and", "the", "a"...- Use the Case Converter node to lower case all words- Use the Snowball Stemmer node to reduce words to the stem- Use the Tag Filter node to delete all words besides adjectives, adverbs and nouns (Tip: See Penn Treebank P.O.S. Tags here: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html)Optional: Use the Document Viewer node to take a look at the preprocessed document Part 1: Reading and Parsing- Read the dataset IMDb-sample.csv (Tip: Drag and drop the dataset from the explorer to theWorkflow Editor)(Tip 2: Change the data type of the column Index to string in theconfiguration window)- Use the Strings to Document node to create documents(Hint: Use the following settings: Title Column = Index Full Text = Text Activate "Use categories from column" and set Document category column = Sentiment)- Use the Column Filter node to delete all columns except thedocument column Optional: Use the Document Viewer node to take a look at thedocuments Part 4: Transformation andFrequencies- Use the Bag Of Words Creator to create a bag ofwords- Use the TF node to calculate the relative termfrequencies- Use the Document Vector node to get a vectorrepresentation of each documentOptional: Calcualute inverse document frequency(IDF) Part 5: Classification- Use the Category To Class node to extract the class labels from the documents- Use the Partitioning node to create a training and test set- Use the Decision Tree Learner node to train a model on the training set- Use the Decision Tree Predictor node to apply the trained decision tree model to the test set- Use the Scorer node to evaluate the modelOptional: Use other algorithms to train a model. Use the ROC Curve to evaluate the model. Filter all columnsexcept the documentcolumnConvert strings toto documentsTerm frequencyRemove punctuationRemovenumbersRemove small wordsTo lower caseReduce to word stemOnly adjectives,adverbs and nounsExtract sentimentlabelTraining / test setApply decisiontree modelRead IMDb reviewsTrain modelPart of speechtaggingCreate documentvectorNode 350Removestop wordsScore DecisionTree modelPOS VisualizationConvert to human readable wordsOnly adjectives,adverbs and nouns Column Filter Strings To Document TF Punctuation Erasure Number Filter N Chars Filter Case Converter Snowball Stemmer Tag Filter Bag Of WordsCreator Category To Class Partitioning Decision TreePredictor File Reader DecisionTree Learner POS Tagger Document Vector Exploratory Viz ROC Curve Stop Word Filter Scorer (JavaScript) Document Viewer Stanford Lemmatizer Tag Filter Part 2:Enrichment- Use the POSTagger node toassign part ofspeech tagsOptional: Use theDocument Viewernode to visualize thetags Part 3: Preprocessing- Use the Punctuation Erasure node to remove punctuation- Use the Number Filter node to remove numbers- Use the N Chars Filter node to remove words with less than 3 characters- Use the Stop Word Filter node to delete words with very little meaning, such as "and", "the", "a"...- Use the Case Converter node to lower case all words- Use the Snowball Stemmer node to reduce words to the stem- Use the Tag Filter node to delete all words besides adjectives, adverbs and nouns (Tip: See Penn Treebank P.O.S. Tags here: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html)Optional: Use the Document Viewer node to take a look at the preprocessed document Part 1: Reading and Parsing- Read the dataset IMDb-sample.csv (Tip: Drag and drop the dataset from the explorer to theWorkflow Editor)(Tip 2: Change the data type of the column Index to string in theconfiguration window)- Use the Strings to Document node to create documents(Hint: Use the following settings: Title Column = Index Full Text = Text Activate "Use categories from column" and set Document category column = Sentiment)- Use the Column Filter node to delete all columns except thedocument column Optional: Use the Document Viewer node to take a look at thedocuments Part 4: Transformation andFrequencies- Use the Bag Of Words Creator to create a bag ofwords- Use the TF node to calculate the relative termfrequencies- Use the Document Vector node to get a vectorrepresentation of each documentOptional: Calcualute inverse document frequency(IDF) Part 5: Classification- Use the Category To Class node to extract the class labels from the documents- Use the Partitioning node to create a training and test set- Use the Decision Tree Learner node to train a model on the training set- Use the Decision Tree Predictor node to apply the trained decision tree model to the test set- Use the Scorer node to evaluate the modelOptional: Use other algorithms to train a model. Use the ROC Curve to evaluate the model. Filter all columnsexcept the documentcolumnConvert strings toto documentsTerm frequencyRemove punctuationRemovenumbersRemove small wordsTo lower caseReduce to word stemOnly adjectives,adverbs and nounsExtract sentimentlabelTraining / test setApply decisiontree modelRead IMDb reviewsTrain modelPart of speechtaggingCreate documentvectorNode 350Removestop wordsScore DecisionTree modelPOS VisualizationConvert to human readable wordsOnly adjectives,adverbs and nouns Column Filter Strings To Document TF Punctuation Erasure Number Filter N Chars Filter Case Converter Snowball Stemmer Tag Filter Bag Of WordsCreator Category To Class Partitioning Decision TreePredictor File Reader DecisionTree Learner POS Tagger Document Vector Exploratory Viz ROC Curve Stop Word Filter Scorer (JavaScript) Document Viewer Stanford Lemmatizer Tag Filter

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Nodes

IMDB_​Sentiment_​With_​Viz consists of the following 70 nodes(s):

Plugins

IMDB_​Sentiment_​With_​Viz contains nodes provided by the following 5 plugin(s):