TY - JOUR
T1 - Multimodal Sentiment Analysis
T2 - Addressing Key Issues and Setting Up the Baselines
AU - Poria, Soujanya
AU - Majumder, Navonil
AU - Hazarika, Devamanyu
AU - Cambria, Erik
AU - Gelbukh, Alexander
AU - Hussain, Amir
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061228693&partnerID=8YFLogxK
U2 - 10.1109/MIS.2018.2882362
DO - 10.1109/MIS.2018.2882362
M3 - Artículo
SN - 1541-1672
VL - 33
SP - 17
EP - 25
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 6
M1 - 8636432
ER -