An optimized hybrid web-based system for sentiment analysis using machine learning

By including visual materials into the study, present work fills in the void in conventional sentiment analysis, which mostly depends on textual data. To find emotional insights from photos, the new method integrates sentiment analysis, semantic web technologies, and sophisticated image processing methods. Deep learning models help to gather visual data including objects, colors, and facial expressions using deep learning models. Then, using ontologies, these characteristics are linked to semantic concepts to create a knowledge network allowing logical process-based emotional deduction. The main discovery is that, especially in context-aware sentiment categorization, our CNN-RESET hybrid strategy beats conventional techniques. This integration of unstructured image data with structured semantic frameworks offers a deeper, more comprehensive understanding, with applications in social media monitoring, advertising, and human-computer interaction.