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.
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Schuller, B., Knaup, T. (2011). Learning and Knowledge-Based Sentiment Analysis in Movie Review Key Excerpts. In: Esposito, A., Esposito, A.M., Martone, R., Müller, V.C., Scarpetta, G. (eds) Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues. Lecture Notes in Computer Science, vol 6456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18184-9_39
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DOI: https://doi.org/10.1007/978-3-642-18184-9_39
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