Skip to main content

SESAM at SemEval-2020 Task 8: Investigating the relationship between image and text in sentiment analysis of memes

Bonheme, Lisa, Grzes, Marek (2020) SESAM at SemEval-2020 Task 8: Investigating the relationship between image and text in sentiment analysis of memes. In: Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020). . (In press) (KAR id:82204)

PDF Author's Accepted Manuscript
Language: English


Creative Commons Licence
This work is licensed under a Creative Commons Attribution 4.0 International License.
Download (358kB) Preview
[img]
Preview
Official URL
http://alt.qcri.org/semeval2020/index.php?id=paper...

Abstract

This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with

two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: sentiment analysis
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Marek Grzes
Date Deposited: 22 Jul 2020 17:15 UTC
Last Modified: 23 Jul 2020 12:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82204 (The current URI for this page, for reference purposes)
Grzes, Marek: https://orcid.org/0000-0003-4901-1539
  • Depositors only (login required):

Downloads

Downloads per month over past year