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Lexicon Based Approach for Sentiment Analysis

NLP Sentiment analysis Text processing Natural Language Processing Practicing Data Science

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This workflow shows how to perform a lexicon 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 tags to words 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 Reference 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 the Association for Computational Linguistics (ACL 2011).

External resources

  • Read more about lexicon-based sentiment analysis: a tutorial

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Textprocessing Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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