Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
Abstract
We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.
Cite
@article{arxiv.1803.07427,
title = {Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines},
author = {Soujanya Poria and Navonil Majumder and Devamanyu Hazarika and Erik Cambria and Alexander Gelbukh and Amir Hussain},
journal= {arXiv preprint arXiv:1803.07427},
year = {2019}
}
Comments
IEEE Intelligence Systems. arXiv admin note: substantial text overlap with arXiv:1707.09538