Journal
IEEE INTELLIGENT SYSTEMS
Volume 33, Issue 6, Pages 17-25Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2018.2882362
Keywords
-
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available