Multimodal Content Analysis Using Deep Learning

Abstract

Abstract: The multimodal content analysis platform combines sentiment analysis and neural style transfertechniques to process and improve various types of digital content. The sentiment analysis module utilizes natural language processing (NLP) algorithms, such as recurrent neural networks (RNNs) or transformer models like BERT, to extract emotional signals from textual, visual, and auditory inputs. Signals are classified into predefined sentiment categories, providing granular insights into the emotional context of the content. The platform employs neural style transfer algorithms, such as style transfer networks (NSTNs) or generative adversarial networks (GANs), to transfer stylistic attributes between texts. By training on a diverse range of artistic styles, the system learns to apply these styles to input text while preserving semantic meaning. This process enhances the visual representation of textual content, making it more appealing and engaging to users.