Multimodal sentiment analysis : addressing key issues and setting up the baselines

• Main Authors: Poria, Soujanya, Majumder, Navonil, Hazarika, Devamanyu, Cambria, Erik, Gelbukh, Alexander, Hussain, Amir
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Sentiment Analysis; Feature Extraction
• Online Access: https://hdl.handle.net/10356/143239
• Institution: Nanyang Technological University

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.