A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Files

hdl_137840.pdf (1.55 MB)
  (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.

License

Grant ID

Call number

Persistent link to this record