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26_​Sentiment_​Analysis_​Lexicon_​Based_​Approach

Lexicon Based Approach for Sentiment Analysis

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).

This workflow shows how to perform a lexicon based approach for sentiment analysis, which can be used in case no labeled data is available. MPQA Dictionaries Color by sentimentlabelCount number of wordsfor each documentCount number ofpositive and negativewords in documentand extract termsAssign PositiveTagsAssign Negative TagsRead articles from IMDB dataseCleaningstandardizationAccuracyPositive listNegative list Color Manager GroupBy Aggregate Dictionary Tagger Dictionary Tagger Calculate Score BoW - TF Read Data Preprocessing Scorer CSV Reader CSV Reader This workflow shows how to perform a lexicon based approach for sentiment analysis, which can be used in case no labeled data is available. MPQA Dictionaries Color by sentimentlabelCount number of wordsfor each documentCount number ofpositive and negativewords in documentand extract termsAssign PositiveTagsAssign Negative TagsRead articles from IMDB dataseCleaningstandardizationAccuracyPositive listNegative listColor Manager GroupBy Aggregate Dictionary Tagger Dictionary Tagger Calculate Score BoW - TF Read Data Preprocessing Scorer CSV Reader CSV Reader

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