A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Files
(Published version)
Date
2020
Authors
Marrese-Taylor, E.
Rodriguez Opazo, C.
Balazs, J.
Gould, S.
Matsuo, Y.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 2020, pp.8-18
Statement of Responsibility
Edison Marrese-Taylor, Cristian Rodriguez-Opazo, Jorge A. Balazs, Stephen Gould and Yutaka Matsuo
Conference Name
58th Annual Meeting of the Association for Computational Linguistics (ACL) (5 Jul 2020 - 10 Jul 2020 : Seattle, USA)
Abstract
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multimodal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.
School/Discipline
Dissertation Note
Provenance
Description
From the Workshop: W19: The Second Grand-Challenge and Workshop on Human Multimodal Language (Challenge-HML)
Access Status
Rights
© 2017 Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.