The proposed Sentiment-oriented Transformer-based Variational Autoencoder (So-TVAE) network can be extended to generate diverse comments for other types of multimedia content beyond live videos by adapting the model architecture and training data. Here are some ways to extend the model:
Multi-modal Data Integration: Incorporate additional modalities such as audio, text, and images to create a more comprehensive understanding of the multimedia content. By including these modalities, the model can generate comments that are more contextually relevant and diverse.
Dataset Expansion: Train the model on diverse datasets containing various types of multimedia content, such as news articles, social media posts, and product reviews. This will enable the model to learn from a wider range of content and generate comments that are suitable for different contexts.
Fine-tuning for Specific Domains: Fine-tune the model on specific domains or genres of multimedia content to tailor the comment generation process to the characteristics of that domain. For example, training the model on movie reviews to generate comments for movie trailers or film clips.
Adaptive Sentiment Analysis: Enhance the sentiment analysis component of the model to adapt to different types of multimedia content and sentiment expressions. This will enable the model to generate comments that are more aligned with the emotional tone of the content.
By incorporating these extensions, the So-TVAE model can be adapted to generate diverse comments for a wide range of multimedia content beyond live videos.