Breaking Down Meme Sentiment Analysis: Text-STILT Triumphs in Performance Study

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Breaking Down Meme Sentiment Analysis: Text-STILT Triumphs in Performance Study
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Explore the impact of unimodal training on meme sentiment analysis through a comparative study, revealing Text-STILT's significance in enhancing performance.

Authors: Muzhaffar Hazman, University of Galway, Ireland; Susan McKeever, Technological University Dublin, Ireland; 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 4 Results 4.

In the case of Bad Luck Brian, see Table 3, a teenage boy smiling in a portrait does not inherently convey tragedy, or misfortune, but this connotation stemmed from the template’s usage in online discourse. In contrast, the unimodal images in Table 3 show a visual language that is less culturally specific, i.e. a serene beach has positive connotations and a disfigured zombie-esque head conveys negative ones.

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