Skip to main content
Kent Academic Repository

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 Fourteenth Workshop on Semantic Evaluation. . (KAR id:82204)

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 withtwo 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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Marek Grzes
Date Deposited: 22 Jul 2020 17:15 UTC
Last Modified: 11 Jan 2024 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82204 (The current URI for this page, for reference purposes)

University of Kent Author Information

Bonheme, Lisa.

Creator's ORCID:
CReDIT Contributor Roles:

Grzes, Marek.

Creator's ORCID: https://orcid.org/0000-0003-4901-1539
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.