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Exercise 2. Keyword_​_​Extraction_​on_​Large_​Movie_​Review_​Dataset

Chapter 4/Exercise 2. Keyword Extraction on Movie Review Dataset
Chapter 4/Exercise 2. Keyword Extraction on Movie Review Dataset Read the Large Movie Review Dataset [1] (sampled) available at the following path Thedata/MoviereviewDataset_sampled.table. 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 and keep only a sample of the documents categorized as positive reviews (first 3 rows). Tag the words available inthe documents and pre-process them by filtering the numbers, erase the punctuations, filter the stop words, convert the words in lower case, apply snowball stemmer and use the Tag Filternode to keep only nouns and verbs. Finally, use the Chi-Square Keyword Extractor node to extract 5 keywords per each document and filter the keywords by using 10 as lower bound for thevariable Chi value. What are the 5 most valuable keywords? To which document they belong to?[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) POStagging- ReadMovie Review Dataset_Labeled& Not_Labeled- Take 3 documents labeled as positive- Number filter- Punctuation Erasure- Stop Word Filter- Case Converter- Snowball Stemmer- Tag FilterKeywords with Chi value > 10Extract 5 keywords from each document Enrichment Reading Data Pre-processing Row Filter Chi-Square KeywordExtractor Chapter 4/Exercise 2. Keyword Extraction on Movie Review Dataset Read the Large Movie Review Dataset [1] (sampled) available at the following path Thedata/MoviereviewDataset_sampled.table. 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 and keep only a sample of the documents categorized as positive reviews (first 3 rows). Tag the words available inthe documents and pre-process them by filtering the numbers, erase the punctuations, filter the stop words, convert the words in lower case, apply snowball stemmer and use the Tag Filternode to keep only nouns and verbs. Finally, use the Chi-Square Keyword Extractor node to extract 5 keywords per each document and filter the keywords by using 10 as lower bound for thevariable Chi value. What are the 5 most valuable keywords? To which document they belong to?[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) POStagging- ReadMovie Review Dataset_Labeled& Not_Labeled- Take 3 documents labeled as positive- Number filter- Punctuation Erasure- Stop Word Filter- Case Converter- Snowball Stemmer- Tag FilterKeywords with Chi value > 10Extract 5 keywords from each documentEnrichment Reading Data Pre-processing Row Filter Chi-Square KeywordExtractor

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Created by: Kilian
Created at: 2014-10-20
On NodePit since: 2021-03-10
Last update: 2024-04-27
Created with KNIME version: v4.6.1
Tags: BooksFrom Words To WisdomText Mining

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