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YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context
May-June 2013 (vol. 28 no. 3)
pp. 46-53
Martin Wollmer, Technische Universität München
Felix Weninger, Technische Universität München
Tobias Knaup, Technische Universität München
Bjorn Schuller, Technische Universität München
Congkai Sun, Shanghai Jiaotong University
Kenji Sagae, University of Southern California
Louis-Philippe Morency, University of Southern California
This work focuses on automatically analyzing a speaker's sentiment in online videos containing movie reviews. In addition to textual information, this approach considers adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.
Index Terms:
Videos,Motion pictures,Pragmatics,Context awareness,Feature extraction,YouTube,Visualization,linguistic analysis,Videos,Motion pictures,Pragmatics,Context awareness,Feature extraction,YouTube,Visualization,intelligent systems,sentiment analysis,affective computing,audio-visual pattern recognition
Citation:
Martin Wollmer, Felix Weninger, Tobias Knaup, Bjorn Schuller, Congkai Sun, Kenji Sagae, Louis-Philippe Morency, "YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context," IEEE Intelligent Systems, vol. 28, no. 3, pp. 46-53, May-June 2013, doi:10.1109/MIS.2013.34
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