  The Controllable Multimodal Feedback Synthesis (CMFeed) dataset enables the
generation of sentiment-controlled feedback from multimodal inputs. It contains
images, text, human comments, comments' metadata and sentiment labels. Existing
datasets for related tasks such as multimodal summarization, visual question
answering, visual dialogue, and sentiment-aware text generation do not
incorporate training models using human-generated outputs and their metadata, a
gap that CMFeed addresses. This capability is critical for developing feedback
systems that understand and replicate human-like spontaneous responses. Based
on the CMFeed dataset, we define a novel task of controllable feedback
synthesis to generate context-aware feedback aligned with the desired
sentiment. We propose a benchmark feedback synthesis system comprising encoder,
decoder, and controllability modules. It employs transformer and Faster R-CNN
networks to extract features and generate sentiment-specific feedback,
achieving a sentiment classification accuracy of 77.23%, which is 18.82% higher
than models not leveraging the dataset's unique controllability features.
Additionally, we incorporate a similarity module for relevance assessment
through rank-based metrics.
