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Learning-Based Sentiment Analysis Using RapidMiner

  • Murugan Anandarajan
  • Chelsey Hill
  • Thomas Nolan
Chapter
Part of the Advances in Analytics and Data Science book series (AADS, volume 2)

Abstract

This chapter provides a step-by-step sentiment analysis in RapidMiner using classification analysis. After being introduced to the RapidMiner software, the reader learns to build a process map-based analysis to classify Amazon reviews by sentiment. Two machine learning methods, k-nearest neighbor and naïve Bayes, are demonstrated and assessed for predictive performance.

Keywords

RapidMiner Sentiment analysis Categorization Classification analysis k-nearest neighbor Naïve Bayes Online consumer reviews 

Reference

  1. Hofmann, M., & Klinkenberg, R. (Eds.). (2013). RapidMiner: Data mining use cases and business analytics applications. Boca Raton: CRC Press.Google Scholar

Further Reading

  1. For more about RapidMiner, see Hofmann and Klinkenberg (2013).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Murugan Anandarajan
    • 1
  • Chelsey Hill
    • 2
  • Thomas Nolan
    • 3
  1. 1.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA
  2. 2.Feliciano School of BusinessMontclair State UniversityMontclairUSA
  3. 3.Mercury Data ScienceHoustonUSA

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