Name
Abstract | ||
|---|---|---|
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the inter-dependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1081 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
Field | DocType | Volume |
Sentiment analysis,Computer science,Natural language processing,Artificial intelligence | Conference | P17-1 |
Citations | PageRank | References |
60 | 1.62 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soujanya Poria | 1 | 1336 | 60.98 |
| Erik Cambria | 2 | 3873 | 183.70 |
| Devamanyu Hazarika | 3 | 132 | 8.19 |
| Navonil Majumder | 4 | 206 | 12.78 |
| Amir Zadeh | 5 | 77 | 3.32 |
| Louis-Philippe Morency | 6 | 3220 | 200.79 |