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Learning and Knowledge-Based Sentiment Analysis in Movie Review Key Excerpts

  • Björn Schuller
  • Tobias Knaup
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6456)

Abstract

We propose a data-driven approach based on back-off N-Grams and Support Vector Machines, which have recently become popular in the fields of sentiment and emotion recognition. In addition, we introduce a novel valence classifier based on linguistic analysis and the on-line knowledge sources ConceptNet, General Inquirer, and WordNet. As special benefit, this approach does not demand labeled training data. Moreover, we show how such knowledge sources can be leveraged to reduce out-of-vocabulary events in learning-based processing. To profit from both of the two generally different concepts and independent knowledge sources, we employ information fusion techniques to combine their strengths, which ultimately leads to better overall performance. Finally, we extend the data-driven classifier to solve a regression problem in order to obtain a more fine-grained resolution of valence.

Keywords

Sentiment Analysis Emotion Recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Björn Schuller
    • 1
  • Tobias Knaup
    • 2
  1. 1.Institute for Human-Machine CommunicationTechnische Universität MünchenGermany
  2. 2.Pingsta Inc.Redwood CityUSA

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