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Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines

2019-02-12 02:42:19
Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Erik Cambria, Alexander Gelbukh, Amir Hussain

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.

Abstract (translated)

URL

https://arxiv.org/abs/1803.07427

PDF

https://arxiv.org/pdf/1803.07427.pdf


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