With the exponential growth of e-commerce, understanding consumer sentiments from online product reviews has become crucial for businesses. This study presents a comprehensive approach to online product review sentiment analysis, encompassing text and image data. To facilitate our research, we compiled a novel dataset from the Amazon e-commerce platform, comprising paired textual reviews, corresponding images and sentiment. The work employs a multimodal deep-learning approach to capture the nuanced sentiment expressed through both text and images. Specifically, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model, enhanced with a low-rank adaptation to refine its self-attention mechanism, for textual sentiment analysis and the VGG16 neural network for image feature extraction. Our experiments demonstrate that this multimodal approach surpasses unimodal techniques, achieving convincing results in sentiment classification.