## 1 Introduction
The goal of this paper is to monitor and discover images and videos linked to terrorism and gambling that are posted on these websites and to discover the public's opinion in real time. Technically, traditional internet-based techniques for monitoring public opinion rely on a single data source and the extraction of text data from web pages. In comparison to multimodal data, the current studies on algorithms for text recognition in videos and images lag behind. Existing methods for monitoring internet public opinion rely primarily on the regular crawling of text information on web pages, which is incapable of rapidly obtaining and recognizing text information in images and videos. In addition, current emotional analysis models cannot deal with the fact that text sequences can have more than one meaning and that information can be lost when intermediate vectors are used to encode features [9]. This can make it hard to understand the context of the modeled features. Based on the preceding, this paper combines the most recent advancements in text recognition with the characteristics of data in public opinion analysis. First, the PP-OCR [10] model deployed in the OpenVINO [11] environment is modified and adapted, and the improved method is able to solve the problem of text recognition in complex scenes, such as videos, while successfully resolving the shortcomings of the original model in terms of its inability to detect distorted images or videos. Then, a new multimodal text recognition and sentiment analysis model is proposed for detecting positive and negative information from different dynamic websites with risky videos or risky images. The existing sentiment analysis model is modified, and the modified text recognition model is combined with the sentiment analysis model. We propose the multimodal text recognition and sentiment analysis model MTR-SAM, which not only improves the most recent PP-OCR model but also introduces a direction-aware function that optimizes the recognition of rotated fonts and improves the detection of distortion and directional skew. This paper proposes a sentiment analysis model that improves the contextual relationship between words and the accuracy of discriminating the sentiment polarity of text. The proposed model for sentiment analysis can strengthen the contextual relationship between words and enhance the precision of text sentiment polarity discrimination. The experimental results show that the new method proposed in this paper improves the recognition speed by 31.7/%, the recognition accuracy by 10.78% on the video dataset, and the F1 value of the sentiment analysis model by 4.42% on the user-generated opinion dataset. In addition, the language of the training set for the multimodal character recognition model is English and Mandarin, whereas the language of the training set for the sentiment analysis model is Mandarin. The model is primarily utilized to monitor Chinese websites, and the method can offer significant technical support for public opinion analysis in multimodal disciplines. Our contribution is in three areas:  
* To address the problem of insufficient text detection models in existing multimodal scenes for large font text or text with an extreme aspect ratio. We proposed the LK-PAN network of large receptive fields in the detection module to improve the CML distillation strategy and then employed an RSE-FPN with a residual attention mechanism. * As a solution to the issue of low accuracy of text recognition models for rotating fonts in existing multimodal scenes, we modified the original CTC decoder to use the CTC (Guided Training of CTC) method and applied a sinusoidal loss function for rotation recognition. This loss function can optimize the model's recognition of rotating fonts and improve text detection performance in settings with arbitrary rotation angles. * In internet public opinion services, public opinion risk detection needs to be conducted on multimodal data, including images and videos.