SemiMemes: A Semi-supervised Learning Approach for Multimodal Memes Analysis (2304.00020v2)

Abstract: The prevalence of memes on social media has created the need to sentiment analyze their underlying meanings for censoring harmful content. Meme censoring systems by machine learning raise the need for a semi-supervised learning solution to take advantage of the large number of unlabeled memes available on the internet and make the annotation process less challenging. Moreover, the approach needs to utilize multimodal data as memes' meanings usually come from both images and texts. This research proposes a multimodal semi-supervised learning approach that outperforms other multimodal semi-supervised learning and supervised learning state-of-the-art models on two datasets, the Multimedia Automatic Misogyny Identification and Hateful Memes dataset. Building on the insights gained from Contrastive Language-Image Pre-training, which is an effective multimodal learning technique, this research introduces SemiMemes, a novel training method that combines auto-encoder and classification task to make use of the resourceful unlabeled data.

• The paper presents a semi-supervised framework that combines text and image modalities to improve meme sentiment analysis using the CROM-AE model.
• It employs the CROM-AE for cross-modality feature reconstruction and the RAW-N-COOK model to integrate latent and original features for nuanced classification.
• Evaluations on the MAMI and Hateful Memes datasets demonstrate superior F1 and AUROC performance with fewer parameters in limited labeled data scenarios.

SemiMemes: Advancements in Multimodal Memes Analysis using Semi-supervised Learning

The research paper titled "SemiMemes: A Semi-supervised Learning Approach for Multimodal Memes Analysis" offers a comprehensive investigation into the domain of memes sentiment analysis with a novel semi-supervised learning framework. The growing ubiquity of memes as a form of communication necessitates advanced methods for analyzing their sentiment, especially when they encapsulate potentially harmful content. This paper introduces a robust learning approach that leverages the power of both labeled and unlabeled data, effectively bridging the gap between supervised and unsupervised methodologies through its innovative model, SemiMemes.

Key Contributions

1. Multimodal Semi-supervised Learning Framework The authors propose SemiMemes, which utilizes both text and visual data from memes to better capture and understand their often complex meanings. This approach is grounded in the insight that meme content cannot be fully understood without analyzing both modalities, addressing a significant challenge in sentiment analysis.
2. CROM-AE Model The Cross Modality Auto Encoder (CROM-AE) plays a central role in this framework, using one data modality to predict and reconstruct the other. Two separate auto-encoders, one for images and another for text, underscore the architecture. This model benefits from unlabeled data, allowing for a deeply informed feature extraction process.
3. RAW-N-COOK Model In the second stage of SemiMemes, the Raw and Cooked Features Classification Model (RAW-N-COOK) is introduced. By incorporating both the learned latent representations from CROM-AE and the original features from CLIP, this model captures more nuanced distinctions in memes than what existing state-of-the-art models can achieve.

Experimental Insights

The paper demonstrates the superiority of SemiMemes over current models through rigorous evaluations on two datasets. On the sub-task B of the Multimedia Automatic Misogyny Identification (MAMI) dataset, which requires multi-label classification of misogynistic content, the SemiMemes framework shows a consistent improvement in the weighted-average F1 scores across varying proportions of labeled data. Similarly, on the Hateful Memes dataset, SemiMemes delivers competitive AUROC scores with significantly fewer parameters than comparable models, emphasizing its efficiency and effectiveness, particularly in scenarios with limited labeled data.

Theoretical and Practical Implications

From a theoretical standpoint, this paper reinforces the utility of semi-supervised learning, especially within contexts involving complex multimodal data. It challenges previous paradigms by suggesting that meaningful and accurate sentiment analysis of memes can be significantly enhanced through semi-supervised approaches. Practically, this framework could be instrumental in refining content moderation systems, allowing platforms to process vast amounts of unannotated meme content more reliably.

Future Directions

The research opens several avenues for future exploration. First, extending the SemiMemes approach to other forms of internet multimedia could yield substantial benefits across digital communication platforms. Furthermore, integrating additional contextual data (such as user interactions or metadata) could enhance the model's predictive capabilities. There is also potential in exploring how these methods might generalize to other domains where understanding nuanced sentiment is critical.

In conclusion, the research paper on SemiMemes presents a significant advancement in the sentiment analysis of memes through a sophisticated semi-supervised approach, combining text and image modalities to effectively analyze and interpret potentially harmful content. As the landscape of digital communication continues to evolve, approaches like SemiMemes could prove foundational in developing systems that are both efficient and scalable.