DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
This work addresses the problem of analyzing multimodal meme data for social media applications, but it is incremental as it builds on existing fusion methods for a specific shared task.
The paper tackled sentiment and humor classification of memes by proposing a system using bimodal fusion techniques, achieving macro F1 scores of 0.357, 0.510, and 0.312 on three tasks, improving over the baseline.
Memes have become an ubiquitous social media entity and the processing and analysis of suchmultimodal data is currently an active area of research. This paper presents our work on theMemotion Analysis shared task of SemEval 2020, which involves the sentiment and humoranalysis of memes. We propose a system which uses different bimodal fusion techniques toleverage the inter-modal dependency for sentiment and humor classification tasks. Out of all ourexperiments, the best system improved the baseline with macro F1 scores of 0.357 on SentimentClassification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of SemanticClasses (Task C).