# Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
## 1 Introduction  
With the release of Midjourney and the open sourcing of the Stable Diffusion model [1] in 2022, \"AI Art\" has become a hot topic on major social media platforms worldwide. AI Art refers to the process of generating and creating images using artificial intelligence algorithms and deep neural network models. Neural network models like the Convolutional Neural Network (CNN), variational autoencoder (VAE), and Generative Adversarial Network (GAN) enable generative models that can comprehend and assimilate abstract features such as painting techniques and styles from existing images, thus facilitating the generation and creation of new images. During its early era, the process of creating images with AI models required a profound comprehension of neural network models for use in network structure design and parameter tuning, making its popularization challenging due to the domain-specific knowledge it demanded. With the advancement of computer vision and natural language processing, models have become capable of acquiring image features at low cost, while neural network structures like the Transformer [2] have improved the understanding of intricate semantics, leading to the rapid development of algorithms such as text-to-image and image-to-image, which in turn has led to the widespread adoption of AI-generated art in the public domain. Today, users can utilize the AI painting functions offered by major platforms to generate and create images using prompt keywords or text. Nevertheless, the advancement of AI Art technology has been met with controversy. The technical intricacies of generative models are not readily comprehensible to the general public, and the limited interpretability during the training process of generative models has also raised skepticism among the public. The utilization of training data may also raise concerns related to legal issues such as copyright, leading to extensive debates on social media platforms such as X and Weibo. As an emerging technology, the benign development of AI Art depends on the widespread dissemination and comprehension of technical details among the general populace. It also necessitates clarification regarding the diverse areas of interest associated with the technology, so as to effectively address the doubts and concerns of the public. Analyzing public opinion regarding the topic of AI Art on social media platforms can provide useful insights into the various attitudes towards AI Art expressed by different online communities, and shed light on the specific technological aspects that concern the public, thereby supporting the formulation of pertinent laws and regulations by relevant authorities, which themselves hold significant practical implications. For public opinion analysis of trending topics on social media platforms, sentiment analysis can be utilized to investigate the sentiments of the general public, aiding in the comprehension of users' opinions, and thereby providing useful insights for further work. In the early era, lexicon-based methods were applied to classifying text sentiments using rule-based methods. On the topic of dementia, [PERSON] et al. [3] collected Weibo posts from 2011 to 2021, and utilized the emotion ontology dictionary provided by DLUT to explore users' discourse and sentiment towards dementia, offering a foundation for decision-making to authorities considering the growing dementia population in China. [PERSON] et al. [4] studied the Weibo posts created during the outbreak of COVID-19 in early 2020, and combined LDA with lexicon-based sentiment analysis to discuss the evolution process of the public's concerns, providing useful insights for public health institutions regarding public opinion. To study public opinion related to COVID-19 vaccination, [PERSON] et al. [5] utilized LDA and lexicon-based sentiment analysis to provide insights into the public's attitudes from detailed topics and sentiments separately.