Unimodal Training for Multimodal Meme Sentiment Classification: Performance Benchmarking

Written by memeology | Published 2024/04/07
Tech Story Tags: meme-sentiment-analysis | text-stilt | unimodal-sentiment-analysis | performance-benchmarking | multimodal-meme-classifiers | sentiment-analysis | sentiment-labeled-data | unimodal-training

TLDRThis study introduces a novel approach, using unimodal training to enhance multimodal meme sentiment classifiers, significantly improving performance and efficiency in meme sentiment analysis.via the TL;DR App

Authors:

(1) Muzhaffar Hazman, University of Galway, Ireland;

(2) Susan McKeever, Technological University Dublin, Ireland;

(3) Josephine Griffith, University of Galway, Ireland.

Table of Links

Abstract and Introduction

Related Works

Methodology

Results

Limitations and Future Works

Conclusion, Acknowledgments, and References

A Hyperparameters and Settings

B Metric: Weighted F1-Score

C Architectural Details

D Performance Benchmarking

E Contingency Table: Baseline vs. Text-STILT

D Performance Benchmarking

Current competing approaches show a small spread of Weigthed F1-scores (see Table 7) and the performance improvement offered by Text-STILT is similarly small. This small range of performances in contemporary approaches suggests that there is still a significant portion of memes that remain a challenge to classify.

This paper is available on arxiv under CC 4.0 license.


Written by memeology | Memes are cultural items transmitted by repetition in a manner analogous to the biological transmission of genes.
Published by HackerNoon on 2024/04/07