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Lexicon_​Based_​Approach_​Beg 3_​SF_​Edited

Lexicon Based Approach for Sentiment Analysis

This workflow shows how to perform a lexycon based approach for sentiment analysis of IMDB reviews dataset. The dataset contains movie reviews, previously labeled as positive/negative. The lexicon based approach assigns a sentiment to each word in a text based on dictionaries of positive and negative words. A sentiment score is then calculated for each document as: (number of positive words - number of negative words) / total number of words.

Lexicon Based Approach for Sentiment Analysis This workflow shows how to perform a lexycon based approach for sentiment analysis of IMDB reviews dataset. The dataset contains movie reviews, previously labelled as positive/negative.The lexicon based approach assigns a sentiment to each word in a text based on dictionaries of positive and negative words. A sentiment score is then calculated for each document as: (number of positive words - number of negative words) / total number of words. Dataset ReferenceAndrew 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 the Associationfor Computational Linguistics (ACL 2011). MPQADictionaries Fix column headersEncode metadataExtract metadata Color by sentimentlabelNode 288Positive listNegative listCount number of wordsfor each documentAccuracyCount number ofpositive and negativewords in documentand extract termscleaningstandardizationAssign PositiveTagsAssign Negative TagsNode 759Node 768Node 772Node 773Node 774Node 775Node 776Node 780Node 781Node 782Color Manager Strings To Document File Reader File Reader GroupBy Scorer Aggregate Pre-processing Dictionary Tagger Dictionary Tagger Calculate Score BoW - TF DuplicateRow Filter Excel Reader (XLS) Excel Writer (XLS) Excel Writer (XLS) Excel Writer (XLS) String Manipulation Document DataExtractor Cell Splitter Column Rename Lexicon Based Approach for Sentiment Analysis This workflow shows how to perform a lexycon based approach for sentiment analysis of IMDB reviews dataset. The dataset contains movie reviews, previously labelled as positive/negative.The lexicon based approach assigns a sentiment to each word in a text based on dictionaries of positive and negative words. A sentiment score is then calculated for each document as: (number of positive words - number of negative words) / total number of words. Dataset ReferenceAndrew 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 the Associationfor Computational Linguistics (ACL 2011). MPQADictionaries Fix column headersEncode metadataExtract metadata Color by sentimentlabelNode 288Positive listNegative listCount number of wordsfor each documentAccuracyCount number ofpositive and negativewords in documentand extract termscleaningstandardizationAssign PositiveTagsAssign Negative TagsNode 759Node 768Node 772Node 773Node 774Node 775Node 776Node 780Node 781Node 782Color Manager Strings To Document File Reader File Reader GroupBy Scorer Aggregate Pre-processing Dictionary Tagger Dictionary Tagger Calculate Score BoW - TF DuplicateRow Filter Excel Reader (XLS) Excel Writer (XLS) Excel Writer (XLS) Excel Writer (XLS) String Manipulation Document DataExtractor Cell Splitter Column Rename

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Created by: Kilian
Created at: 2014-10-20
On NodePit since: 2022-02-06
Last update: 2024-04-27
Created with KNIME version: v4.1.2
Tags: NLPsentiment analysistext processingNatural Language Processing

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