Multimodal Sentiment Analysis: Perceived vs Induced Sentiments
This work addresses sentiment analysis for social media users by analyzing GIFs, but it is incremental as it builds on existing methods with specific enhancements.
The researchers tackled the problem of predicting sentiment in GIFs by developing a multimodal framework that integrates visual and textual features, achieving an accuracy of 82.7% on Twitter GIFs, which improves over state-of-the-art models.
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