  Social media has created a global network where people can easily access and
exchange vast information. This information gives rise to a variety of
opinions, reflecting both positive and negative viewpoints. GIFs stand out as a
multimedia format offering a visually engaging way for users to communicate. In
this research, we propose a multimodal framework that integrates visual and
textual features to predict the GIF sentiment. It also incorporates attributes
including face emotion detection and OCR generated captions to capture the
semantic aspects of the GIF. The developed classifier achieves an accuracy of
82.7% on Twitter GIFs, which is an improvement over state-of-the-art models.
Moreover, we have based our research on the ReactionGIF dataset, analysing the
variance in sentiment perceived by the author and sentiment induced in the
reader
