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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2015: Machine Learning and Data Mining in Pattern Recognition pp 87–97Cite as

SentiSAIL: Sentiment Analysis in English, German and Russian

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9166))

Abstract

Sentiment analysis has been well in the focus of researchers in recent years. Nevertheless despite the considerable amount of literature in the field the majority of publications target the domains of movie and product reviews in English. The current paper presents a novel sentiment analysis method, which extends the state-of-the-art by trilingual sentiment classification in the domains of general news and particularly the coverage of natural disasters in general news. The languages examined are English, German and Russian. The approach targets both traditional and social media content. The extensive experiments demonstrate that the performance of the proposed approach outperforms human annotators, as well as the original method, on which it is built and extended.

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Notes

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Authors and Affiliations

  1. SAIL LABS Technology GmbH, Vienna, Austria

    Gayane Shalunts & Gerhard Backfried

Authors
  1. Gayane Shalunts
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  2. Gerhard Backfried
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Corresponding author

Correspondence to Gayane Shalunts .

Editor information

Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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© 2015 Springer International Publishing Switzerland

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Shalunts, G., Backfried, G. (2015). SentiSAIL: Sentiment Analysis in English, German and Russian. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-21024-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21023-0

  • Online ISBN: 978-3-319-21024-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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