Publication
Title
Aspect-based emotion analysis and multimodal coreference : a case study of customer comments on Adidas instagram posts
Author
Abstract
While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA). Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. The presented dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information. However, when more specific information about the aspects is desired, image recognition could be essential.
Language
English
Source (book)
Proceedings of the Thirteenth Language Resources and Evaluation Conference, 20-25 June, 2022, Marseille, France
Publication
European Language Resources Association , 2022
ISBN
979-1-0955-4672-6
Volume/pages
p. 574-580
Full text (open access)
UAntwerpen
Publication type
Subject
External links
Source file
Record
Identifier
Creation 06.11.2023
Last edited 17.06.2024
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