MMS-CLIP: A Robust Framework for Multimodal Memecoin Market Sentiment Analysis

With the rapid rise of memecoins such as Dogecoin ($DOGE) in the cryptocurrency market, their price volatility has become highly dependent on discussions and trends across social media platforms. The memecoin market is heavily influenced by celebrity endorsements, community sentiment, and short-term events. Hence, monitoring and understanding this sentiment is crucial but challenged by data volume, noise (missing modalities), and domain-specific visual/textual language (memes, slang). We propose MMS-CLIP, a robust multimodal market sentiment analysis framework leveraging the Contrastive Language-Image Pre-Training (CLIP) model enhanced with a novel Mask Token mechanism to improve complementarity between modalities to handle real-world data incompleteness. MMS-CLIP outperforms existing baseline models on the real-world social media dataset MVSA-Single. To facilitate research into the specific challenges of meme-driven sentiment, we introduce SESAME-1k, a novel dataset comprising annotated data from Reddit meme coin posts, capturing the unique signal characteristics of this domain. The code is available at https://github.com/MO7YW4NG/MMS-CLIP.