Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data

The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathetic, accurate, and engaging responses, with useful applications in education, healthcare, marketing, and customer service. To this end, we have constructed a large-scale Controllable Multimodal Feedback Synthesis (CMFeed) dataset and propose a controllable feedback synthesis system. The system features an encoder, decoder, and controllability block for textual and visual inputs. It extracts features using a transformer and Faster R-CNN networks, combining them to generate feedback. The CMFeed dataset includes images, texts, reactions to the posts, human comments with relevance scores, and reactions to these comments. These reactions train the model to produce feedback with specified sentiments, achieving a sentiment classification accuracy of 77.23\%, which is 18.82\% higher than the accuracy without controllability. The system also incorporates a similarity module for assessing feedback relevance through rank-based metrics and an interpretability technique to analyze the contributions of textual and visual features during feedback generation. Access to the CMFeed dataset and the system's code is available at https://github.com/MIntelligence-Group/CMFeed.