%0 Conference Proceedings %T A domain-agnostic approach for opinion prediction on speech %A Santos, Pedro Bispo %A Beinborn, Lisa %A Gurevych, Iryna %Y Nissim, Malvina %Y Patti, Viviana %Y Plank, Barbara %S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES) %D 2016 %8 December %I The COLING 2016 Organizing Committee %C Osaka, Japan %F santos-etal-2016-domain %X 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. %U https://aclanthology.org/W16-4318 %P 163-172