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Title: Multimodal sentiment analysis : addressing key issues and setting up the baselines
Authors: Poria, Soujanya
Majumder, Navonil
Hazarika, Devamanyu
Cambria, Erik
Gelbukh, Alexander
Hussain, Amir
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Poria, S., Majumder, N., Hazarika, D., Cambria, E., Gelbukh, A., & Hussain, A. (2018). Multimodal sentiment analysis : addressing key issues and setting up the baselines. IEEE Intelligent Systems, 33(6), 17-25. doi:10.1109/MIS.2018.2882362
Journal: IEEE Intelligent Systems 
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.
ISSN: 1541-1672
DOI: 10.1109/MIS.2018.2882362
Rights: © 2018 IEEE (published by the IEEE Computer Society). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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