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.
@article{arxiv.1707.09538,
title = {Benchmarking Multimodal Sentiment Analysis},
author = {Erik Cambria and Devamanyu Hazarika and Soujanya Poria and Amir Hussain and R. B. V. Subramaanyam},
journal= {arXiv preprint arXiv:1707.09538},
year = {2017}
}