Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn sentiments in different modal data; (2) they employ text and image pre-trained models trained in different unimodal tasks to extract different modal features, so that the extracted features cannot take into account the interactive information between image and text. To solve these problems, in this paper we propose a Vision-Language Contrastive Learning network (VLCLNet). First, we introduce a pre-trained Large Language Model (LLM), which is trained from vast quantities of multimodal data, has better understanding ability for image and text contents, thus being effectively applied to different tasks while requiring few amount of labelled training data. Second, we adapt a Multimodal Large Language Model (MLLM), BLIP-2 (Bootstrapping Language-Image Pre-training) network, to extract multimodal fusion feature. Such MLLM can fully consider the correlation between images and texts when extracting features. In addition, due to the discrepancy between the pre-training task and the sentiment analysis task, the pre-trained model will output the suboptimal prediction results. We use Low-Rank Adaptation (LoRA) fine-tuning strategy to update the model parameters on sentiment analysis task, which avoids the issue of inconsistent task between pre-training task and downstream task. Experiments verify that the proposed VLCLNet is superior to other strong baselines.

Multimodal aspect-level sentiment analysis has attracted increasing attention in recent years. However, existing methods have two unaddressed limitations: (1) due to the lack of labelled pre-training data of dedicated sentiment analysis, the methods with a pre-training manner produce suboptimal prediction results; (2) most existing methods employ a self-attention encoder to fuse multimodal tokens, which not only ignores the alignment relationship between different modal tokens but also makes the model unable to capture the semantic links between images and texts. In this paper, we propose a momentum contrastive learning network (MOCOLNet) to overcome above limitations. First, we merge the pre-training stage with the training stage to design an end-to-end training manner which uses less labelled data dedicated to sentiment analysis to obtain better prediction results. Second, we propose a multimodal contrastive learning method to align the different modal representations before data fusing, and design a cross-modal matching strategy to provide semantic interactive information between texts and images. Moreover, we introduce an auxiliary momentum strategy to increase the robustness of model. We also analyse the effectiveness of the proposed multimodal contrastive learning method using a mutual information theory. Experiments verify that the proposed MOCOLNet is superior to other strong baselines.