Meme Sentiment Analysis: A Comparative Study of Traditional Multimodal Methods and Vision-Language Large Models

Original Author/Maintainer: mnovgorodtsev Source Platform: GitHub Original Title: MemeSentiment Original Link: https://github.com/mnovgorodtsev/MemeSentiment Publication Date: 2026-05-30

This article focuses on the performance comparison between traditional multimodal methods and vision-language large models in the task of meme sentiment analysis, analyzing the advantages and limitations of the two types of methods in understanding image-text interactive content, while covering experimental design, application prospects, and future research directions.

Memes are network communication carriers that combine images and text. Conveying complex information requires simultaneous processing of visual and textual content as well as their interaction, making it a challenging task in the multimodal field. Traditional unimodal methods struggle to accurately capture emotional tendencies (e.g., neutral images with sarcastic text), so multimodal methods need to fuse image and text features to improve accuracy.

Traditional Multimodal Methods

• Strategy: Extract image (ResNet/VGG) and text (BERT/Word2Vec) features in stages, fuse them via concatenation/attention/bilinear pooling, and input to a classifier for prediction.
• Advantages: Simple structure, efficient computation, strong interpretability;
• Limitations: Separation of feature extraction and fusion, difficulty in capturing deep interactions, limited transferability of pre-training, and poor performance on complex reasoning tasks.

Vision-Language Large Models (e.g., CLIP, BLIP, LLaVA)

• Strategy: Pre-trained on large-scale image-text pairs, using self-attention to capture fine-grained cross-modal interactions;
• Advantages: Strong semantic understanding, zero-shot/few-shot learning capabilities, ability to understand image-text semantic relationships and reasoning, and outstanding performance on content sensitive to cultural background/context.

• Evaluation Dimensions: Accuracy (classification accuracy, precision/recall), robustness (noise/occlusion/style adaptation), efficiency (inference speed/resource requirements), interpretability (attention distribution/decision basis);
• Datasets: Commonly used datasets like Hateful Memes, Memotion, etc. It is necessary to ensure that both types of methods are evaluated under the same training and testing conditions to ensure fairness.

• Overall Performance: Large models significantly outperform traditional methods on most standard datasets and are better at handling complex expressions like sarcasm and metaphors;
• Value of Traditional Methods: Practical in resource-constrained scenarios, and perform well on specific memes (vision-dominated, simple text);
• Limitations of Large Models: Poor performance on culture-specific humor or emerging internet terms due to the timeliness limitations of training data, requiring continuous updates or adaptive learning.

• Application Prospects: Content moderation (identifying harmful memes), marketing (analyzing emotional responses to brand memes), mental health (monitoring users' emotional changes);
• Future Directions: Developing lightweight large models (for mobile real-time operation), multilingual meme understanding, enhancing cultural/current event understanding with knowledge graphs, and personalized meme generation technology.

• Entry Suggestions: First master the basics of traditional methods (feature extraction, fusion mechanisms), then learn about large models (pre-training strategies, fine-tuning methods);
• Research Trends: The two types of methods are complementary; combining the representation ability of large models with the efficiency of traditional methods can develop accurate and practical analysis systems;
• Key Focus: Not only focus on performance indicators but also pay attention to interpretability and practical deployment feasibility.