## 3 Methodology  
In the following chapters, we will provide a comprehensive explanation of the multimodal text recognition and sentiment analysis model (MTR-SAM) that we have proposed. In this paper, we solve the problem of insufficient text detection models in existing multimodal scenes for large font text or text with an extreme aspect ratio. We solve the issue of low accuracy of text recognition models for rotating fonts in existing multimodal scene font text. Secondly, we solve the technical problem that existing sentiment analysis models are unable to well solve the problem of multiple meanings of the word text sequences and the information loss of intermediate vectors during feature encoding, which will lead to a lack of contextualization of the modeled features. Thus, to solve the above issues and combine data characteristics in opinion analysis, we will first improve and modify the OpenVINO-deployed PP-OCR model based on the latest text recognition advances, and then we will improve and modify the sentiment analysis model. Finally, the model for better text recognition and the model for analyzing sentiment are combined, and then a new model for multimodal text recognition and sentiment analysis is designed. The work is manifested in three areas: (1) The LK-PAN network with large sensory fields is proposed in the detection module to upgrade the CML distillation strategy, followed by an RSE-FPN with a residual attention mechanism to enhance the characterization of the feature maps. By improving the text detection module, we can address the problem of insufficient text detection models in existing multimodal scenes for large font text or text with an extreme aspect ratio. (2) The original CTC decoder is changed to the GTC (Guided Training of CTC) method to further enhance the inference of the model for multimodal text recognition accuracy. A sinusoidal loss function for rotation recognition is also introduced, which can optimize the model's recognition of rotated fonts and enhance the performance of text detection for scenes with arbitrary rotation angles. By improving the text recognition module, we can solve the issue of the low accuracy of text recognition models for rotating fonts in existing multimodal scenes. (3) We propose the introduction of a sentiment analysis model for distinguishing multimodal recognition results. First, the BERT model is adopted to acquire high-quality semantic encoding to solve the problem of a single word having multiple meanings. Second, a combination of global and local information block attention mechanisms is proposed as a solution to the problem that word vectors readily lose word positions during the splicing process and can result in information loss in intermediate vectors during encoding. The proposed model can assist public opinion managers with intelligent monitoring. The structure diagram of the improved model is shown in Figure 1.  
In Figure 1 above, the datasets of images and videos are prepared in the first stage as the input for the training model. Second, after the second stage of the inference acceleration engine, which mainly includes two parts, the model optimizer and inference engine, can accelerate the reading and inference of input images and videos by using text detection and recognition models. Then, in the third stage, the three modules OpenCV, Media SDK, and Processing optimize the input information (image and video data) from the previous phase in order to improve the image and video quality. Immediately thereafter, the image and video data are put through feature extraction by the VGG 19 and ResNet 101 networks, respectively, and the extracted features are mutually combined with the third-stage output information as the subsequent input. In the fourth-stage text detection module, the LK-PAN and DML strategies are added, and in the text recognition module, the TextRotNet network and GTC strategies are added. Then, the improved sentiment analysis model is pretrained on the self-built opinion dataset in this paper.