# Benchmarking Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/1707.09538 · HTML: https://mlsanity.com/paper/1707.09538/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2017.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2312.05735/) (2023)
- [A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition](https://mlsanity.com/paper/2306.17799/) (2023)
- [M-SENA: An Integrated Platform for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2203.12441/) (2022)
- [On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era](https://mlsanity.com/paper/2104.10121/) (2021)
- [DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam](https://mlsanity.com/paper/2106.04853/) (2021)
- [A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis](https://mlsanity.com/paper/2103.02636/) (2021)
- [DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis](https://mlsanity.com/paper/2504.11082/) (2025)
- [Evaluation of data inconsistency for multi-modal sentiment analysis](https://mlsanity.com/paper/2406.03004/) (2024)
- [PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2312.12334/) (2023)
- [Robust Multimodal Sentiment Analysis via Double Information Bottleneck](https://mlsanity.com/paper/2511.01444/) (2025)
- [A Multimodal Sentiment Dataset for Video Recommendation](https://mlsanity.com/paper/2109.08333/) (2021)

## Abstract (arXiv)

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal sentiment analysis research: the role of speaker-independent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal sentiment analysis and also demonstrates the different facets of analysis to be considered while performing such tasks.