Affective Feedback Synthesis Towards Multimodal Text and Image Data

Abstract

In this article, we have defined a novel task of affective feedback synthesis that generates feedback for input text and corresponding images in a way similar to humans responding to multimodal data. A feedback synthesis system has been proposed and trained using ground-truth human comments along with image–text input. We have also constructed a large-scale dataset consisting of images, text, Twitter user comments, and the number of likes for the comments by crawling news articles through Twitter feeds. The proposed system extracts textual features using a transformer-based textual encoder. The visual features have been extracted using a Faster region-based convolutional neural networks model. The textual and visual features have been concatenated to construct multimodal features that the decoder uses to synthesize the feedback. We have compared the results of the proposed system with baseline models using quantitative and qualitative measures. The synthesized feedbacks have been analyzed using automatic and human evaluation. They have been found to be semantically similar to the ground-truth comments and relevant to the given text–image input.