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arXiv · 2502.02938

LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier

T. Chay-intr·Y. Chen·K. Viriyayudhakorn·T. Theeramunkong
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Abstract

We present LLaVAC, a method for constructing a classifier for multimodal sentiment analysis. This method leverages fine-tuning of the Large Language and Vision Assistant (LLaVA) to predict sentiment labels across both image and text modalities. Our approach involves designing a structured prompt that incorporates both unimodal and multimodal labels to fine-tune LLaVA, enabling it to perform sentiment classification effectively. Experiments on the MVSA-Single dataset demonstrate that LLaVAC outperforms existing methods in multimodal sentiment analysis across three data processing procedures. The implementation of LLaVAC is publicly available at https://github.com/tchayintr/llavac.

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T. Chay-intr, Y. Chen, K. Viriyayudhakorn, T. Theeramunkong. 2025-02-05. LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier. https://arxiv.org/abs/2502.02938

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