A domain-agnostic approach for opinion prediction on speech

Pedro Bispo Santos, Lisa Beinborn, Iryna Gurevych


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.
Anthology ID:
W16-4318
Volume:
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
PEOPLES | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
163–172
Language:
URL:
https://aclanthology.org/W16-4318
DOI:
Bibkey:
Cite (ACL):
Pedro Bispo Santos, Lisa Beinborn, and Iryna Gurevych. 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), pages 163–172, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
A domain-agnostic approach for opinion prediction on speech (Santos et al., 2016)
Copy Citation:
PDF:
https://aclanthology.org/W16-4318.pdf
Code
 UKPLab/coling-peoples2016-opinion-prediction