Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/mis.2018.2882362 | IEEE Intelligent Systems |
Keywords | DocType | Volume |
Sentiment analysis,Feature extraction,Visualization,Emotion recognition,Affective computing,Social networking (online),Intelligent systems | Journal | abs/1803.07427 |
Issue | ISSN | Citations |
6 | 1541-1672 | 4 |
PageRank | References | Authors |
0.40 | 22 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soujanya Poria | 1 | 1336 | 60.98 |
| Navonil Majumder | 2 | 206 | 12.78 |
| Devamanyu Hazarika | 3 | 132 | 8.19 |
| Erik Cambria | 4 | 3873 | 183.70 |
| Alexander Gelbukh | 5 | 2843 | 269.19 |
| Alexander Gelbukh | 6 | 2843 | 269.19 |