# Multimodal Sentiment Analysis: Perceived vs Induced Sentiments

arXiv: https://arxiv.org/abs/2312.07627 · HTML: https://mlsanity.com/paper/2312.07627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [TGIF: A New Dataset and Benchmark on Animated GIF Description](https://mlsanity.com/paper/1604.02748/) (2016)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [Emotion Detection in Text: a Review](https://mlsanity.com/paper/1806.00674/) (2018)
- [Distant supervision for emotion detection using Facebook reactions](https://mlsanity.com/paper/1611.02988/) (2016)
- [Visual Sentiment Prediction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1411.5731/) (2014)
- [Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter](https://mlsanity.com/paper/2105.09967/) (2021)

## Abstract (arXiv)

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