An Information Gain-Driven Feature Study for Aspect-Based Sentiment Analysis

  • Kim SchoutenEmail author
  • Flavius Frasincar
  • Rommert Dekker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9612)


Nowadays, opinions are a ubiquitous part of the Web and sharing experiences has never been more popular. Information regarding consumer opinions is valuable for consumers and producers alike, aiding in their respective decision processes. Due to the size and heterogeneity of this type of information, computer algorithms are employed to gain the required insight. Current research, however, tends to forgo a rigorous analysis of the used features, only going so far as to analyze complete feature sets. In this paper we analyze which features are good predictors for aspect-level sentiment using Information Gain and why this is the case. We also present an extensive set of features and show that it is possible to use only a small fraction of the features at just a minor cost to accuracy.


Sentiment analysis Aspect-level sentiment analysis Data mining Feature analysis Feature selection Information gain 



The authors are supported by the Dutch national program COMMIT. We would like to thank Nienke Dijkstra, Vivian Hinfelaar, Isabelle Houck, Tim van den IJssel, and Eline van de Ven, for many fruitful discussions during this research.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kim Schouten
    • 1
    Email author
  • Flavius Frasincar
    • 1
  • Rommert Dekker
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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