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Encyclopedia of Machine Learning and Data Mining pp 938–947Cite as

Opinion Stream Mining

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Abstract

Opinion stream mining aims at learning and adaptation of a polarity model over a stream of opinionated documents, i.e., documents associated with a polarity. They comprise a valuable tool to analyze the huge amounts of opinions generated nowadays through the social media and the Web. In this chapter, we overview methods for polarity learning in a stream environment focusing especially on how these methods deal with the challenges imposed by the stream nature of the data, namely the nonstationary data distribution and the single pass constraint.

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Notes

  1. 1.

    Work partially done while with the Ludwig-Maximilians University, Munich.

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Author information

Authors and Affiliations

  1. Otto-von-Guericke University-Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  2. Leibniz Universität Hannover, Hannover, Germany

    Eirini Ntoutsi

  3. Ludwig Maximilians Universität München, Munich, Germany

    Eirini Ntoutsi

  4. Swedish Institute of Computer Science (SICS Swedish ICT), Kista, Sweden

    Max Zimmermann

Authors
  1. Myra Spiliopoulou
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  2. Eirini Ntoutsi
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  3. Max Zimmermann
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Corresponding author

Correspondence to Eirini Ntoutsi .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Spiliopoulou, M., Ntoutsi, E., Zimmermann, M. (2017). Opinion Stream Mining. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_905

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