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A domain-agnostic approach for opinion prediction on speech

Santos, Pedro Bispo and Beinborn, Lisa and Gurevych, Iryna
Nissim, Malvina and Patti, Viviana and Plank, Barbara (eds.) (2016):
A domain-agnostic approach for opinion prediction on speech.
In: Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), The COLING 2016 Organizing Committee, Osaka, Japan, ISBN 978-4-87974-723-5,
[Online-Edition: http://www.aclweb.org/anthology/W16-4318],
[Conference or Workshop Item]

Abstract

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state- of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.

Item Type: Conference or Workshop Item
Erschienen: 2016
Editors: Nissim, Malvina and Patti, Viviana and Plank, Barbara
Creators: Santos, Pedro Bispo and Beinborn, Lisa and Gurevych, Iryna
Title: A domain-agnostic approach for opinion prediction on speech
Language: English
Abstract:

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state- of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.

Title of Book: Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
Publisher: The COLING 2016 Organizing Committee
ISBN: 978-4-87974-723-5
Uncontrolled Keywords: reviewed;UKP_reviewed;UKP_p_AudioVisual
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Osaka, Japan
Date Deposited: 31 Dec 2016 14:29
Official URL: http://www.aclweb.org/anthology/W16-4318
Identification Number: TUD-CS-2016-14666
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