SenticSpace: Visualizing Opinions and Sentiments in a Multi-dimensional Vector Space

  • Erik Cambria
  • Amir Hussain
  • Catherine Havasi
  • Chris Eckl
Conference paper

DOI: 10.1007/978-3-642-15384-6_41

Volume 6279 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Cambria E., Hussain A., Havasi C., Eckl C. (2010) SenticSpace: Visualizing Opinions and Sentiments in a Multi-dimensional Vector Space. In: Setchi R., Jordanov I., Howlett R.J., Jain L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science, vol 6279. Springer, Berlin, Heidelberg

Abstract

In a world in which millions of people express their feelings and opinions about any issue in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information is a challenging task. In this work we build a knowledge base which merges common sense and affective knowledge and visualize it in a multi-dimensional vector space, which we call SenticSpace. In particular we blend ConceptNet and WordNet-Affect and use dimensionality reduction on the resulting knowledge base to build a 24-dimensional vector space in which different vectors represent different ways of making binary distinctions among concepts and sentiments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Erik Cambria
    • 1
  • Amir Hussain
    • 1
  • Catherine Havasi
    • 2
  • Chris Eckl
    • 3
  1. 1.Dept. of Computing Science and MathsUniversity of StirlingScotland, UK
  2. 2.MIT Media LabMITUSA
  3. 3.Sitekit Labs, Sitekit Solutions LtdScotland, UK