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Exercise 3. DocumentVectorHashing_​Creation_​on_​Large_​Movie_​Review_​Dataset

Chapter 4/Exercise 3. Document Vector Hashing Creation from the Movie Review Dataset
Chapter 4/Exercise 3. Document Vector Hashing Creation from the Movie Review Dataset Read the Large Movie Review Dataset [1]. The dataset contains labeled reviews as positive or negative, as well unlabeled reviews.Use the Strings to Document node to transforms the strings into documents. Tag all the words available in the documents and pre-process them by filtering the numbers, erase thepunctuations, filter the stop words, convert the words in lower case, apply snowball stemmer and use the Tag Filter node to keep only the tagged words. Create the bag of words for the termsthat have been tagged. Continue the analysis by filtering the Bag of Words to keep only the terms that occur at least 5 times in the documents. Split the collection of documents in two differentsets of data. The Rule-based Row Splitter node needs to split the data in a way that the labels NEG or POS in the variable Category are available in the top output port. Instead the bottomoutput port should contain only the missing values for the variable Category. [1] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of theAssociation for Computational Linguistics (ACL 2011) Document Vector Hashing Creator creates BoWfrom Documents(1)Filter Bag of WordsKeep only terms thatoccur in at least 5documents(2) Extract Category (labels NEG and POS)Transform collection of documents to vector spaceApply the document vector model to the unlabeled rowsReadMovie Review Dataset_Labeled& Not_LabeledPOS taggingNumber filterPunctuation ErasureStop Word FilterCase ConverterSnowball StemmerTag FilterLabeled rows ->topUnlabeled rows -> bottom Bag Of WordsCreator Pre-processing II DocumentVector Hashing Document VectorHashing Applier Reading Data Enrichment Pre-processing Rule-basedRow Splitter Chapter 4/Exercise 3. Document Vector Hashing Creation from the Movie Review Dataset Read the Large Movie Review Dataset [1]. The dataset contains labeled reviews as positive or negative, as well unlabeled reviews.Use the Strings to Document node to transforms the strings into documents. Tag all the words available in the documents and pre-process them by filtering the numbers, erase thepunctuations, filter the stop words, convert the words in lower case, apply snowball stemmer and use the Tag Filter node to keep only the tagged words. Create the bag of words for the termsthat have been tagged. Continue the analysis by filtering the Bag of Words to keep only the terms that occur at least 5 times in the documents. Split the collection of documents in two differentsets of data. The Rule-based Row Splitter node needs to split the data in a way that the labels NEG or POS in the variable Category are available in the top output port. Instead the bottomoutput port should contain only the missing values for the variable Category. [1] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of theAssociation for Computational Linguistics (ACL 2011) Document Vector Hashing Creator creates BoWfrom Documents(1)Filter Bag of WordsKeep only terms thatoccur in at least 5documents(2) Extract Category (labels NEG and POS)Transform collection of documents to vector spaceApply the document vector model to the unlabeled rowsReadMovie Review Dataset_Labeled& Not_LabeledPOS taggingNumber filterPunctuation ErasureStop Word FilterCase ConverterSnowball StemmerTag FilterLabeled rows ->topUnlabeled rows -> bottom Bag Of WordsCreator Pre-processing II DocumentVector Hashing Document VectorHashing Applier Reading Data Enrichment Pre-processing Rule-basedRow Splitter

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Created by: Kilian
Created at: 2014-10-20
On NodePit since: 2021-03-10
Last update: 2024-04-18
Created with KNIME version: v4.5.0
Tags: BooksText MiningFrom Words To WisdomExericise

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