# Research on Online Review Information Classification Based on Multimodal Deep Learning
## 2 Related Works  
This section presents a review of the field of sentiment categorization of online review information, highlighting the main areas of research, analytical methods, and conclusions, with a literature review to understand the contributions and applications of each method in their respective areas of research. Research in the field of multimodal sentiment analysis has been extensively explored in an attempt to combine data of different modalities (e.g., text, image, audio, etc.) to improve the accuracy and comprehensiveness of sentiment analysis tasks. And previous research findings have shown that multimodal sentiment analysis methods that combine different modal data have better performance compared to single-modal methods. The combined use of multimodal data provides a richer and more comprehensive representation of sentiment and helps to improve the accuracy and robustness of sentiment classification. Research on multimodal sentiment analysis has used various approaches to integrate multimodal data, including feature-level fusion, model-level fusion, and attention mechanisms. Deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms have been widely used for multimodal sentiment analysis tasks. The existing findings suggest that combining multimodal data can improve the performance of sentiment analysis, especially in terms of categorizing and understanding sentiments at a more granular level. Deep learning frameworks have shown good results in multimodal sentiment analysis, which helps to exploit the association and complementarity between different data modalities. [PERSON] (2002) first applied the machine learning method of N-Gram to the field of sentiment analysis, and the experimental results show that N-Gram achieved the highest classification accuracy of 81.9% [10]. Since feature selection affects the performance of machine learning methods, [PERSON] (2016) analyzed online review comments through the N-gram model combined with machine learning methods, and the experimental results show that the SVM combined with unigram, bigram, and trigram features obtained the best classification results [11]. Traditional multi-scalar sentiment categorization methods mainly include two routes: sentiment dictionary-based and machine learning-based. Sentiment lexicon-based methods usually calculate sentiment polarity on a whole-sentence or sub-sentence basis and apply it to all facets involved in a sentence, but they cannot handle the complex mapping relationship between sentences and facets well [12; 13]. The core of this approach is the application of affective lexicons; however, there are differences in the affective tendencies of affective words in different domains, making it difficult to generalize domain-specific affective lexicons to other domains [14; 15]. In particular, [PERSON] et al. used a short text classification model based on BERT, which utilizes BERT's own sentence vector training to achieve automatic text classification [16; 17]. The main objective of multimodal learning is to correlate and process multimodal data by building models. According to the research summary of related scholars, the main content of current multimodal research can be summarized as five levels of multimodal data representation, namely data mapping, data alignment, data fusion, and co-learning [18; 19]. Most of it revolves around cross-modal mapping between data. As the fields of computer vision and natural language processing continue to evolve, and as more large-scale datasets become available for research, multimodal data mapping methods continue to mature [20]. The mainstream multimodal data mapping method is as follows: based on the existing mapping relationship, the existing multimodal data are first symbolized or vectorized, and are then used as the input to the neural network, and then combined with the existing correspondences, mapped to another modal.