Noise Classification from Multi-modal feedback in the Product and Service Industry for Sentiment Analysis

Sentimental Analysis utilizes multi-modal feedback that combining audio, video, and text. It is probably uses for emotion analysis. However, noise in the data across all modalities poses significant challenges for prediction precision and reliability. This paper focuses on noise classification and reduction for multi-modal feedback to facilitate better performance in sentiment analysis. Advanced transformer-based architectures are used for the extractions of features from audio, visual, and textual data with robust sentiment prediction by cross-modal fusion. It detects noise features using noisy feature suppression and reduces complexity by attention mechanisms. This work highlights the need for sophisticated methods for noise reduction in achieving the models for reliable, scalable, and context-aware sentiment analysis for applications in customer satisfaction analysis and product reviews, among other domain-specific sentiment studies.