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Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines.
- Source :
- IEEE Intelligent Systems; Nov/Dec2018, Vol. 33 Issue 6, p17-25, 9p
- Publication Year :
- 2018
-
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., the role of speaker-exclusive models, the 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 FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15411672
- Volume :
- 33
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Intelligent Systems
- Publication Type :
- Academic Journal
- Accession number :
- 134601929
- Full Text :
- https://doi.org/10.1109/MIS.2018.2882362