Name
Abstract | ||
|---|---|---|
With the rapid development of mobile Internet, and the popularization of e-commerce and social networks, people have changed from the simple users of network information to the main publishers of network information. Thus, a large number of various network data have been generated, and a large part of these data contains negative emotions. Mining these data can make us better understand the views and positions of netizens, and help to grasp the key information of network public opinion. In this paper, we provide an introduction for the background knowledge of emotion analysis, including different definitions and classification methods of emotion. Then, we summarize the related models of deep learning, as well as the main emotion analysis methods in text based on deep learning, and make a detailed introduction and comparison on these methods. Finally, we enumerate the challenges of emotion analysis in text, and the future research trend for emotion analysis. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/iSCI50694.2020.00020 | 2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) |
Keywords | DocType | ISBN |
Text,emotion analysis,deep learning,social networks,public opinion | Conference | 978-1-6654-0395-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihong Cao | 1 | 59 | 3.65 |
| Sancheng Peng | 2 | 234 | 19.59 |
| Pengfei Yin | 3 | 0 | 0.34 |
| Yongmei Zhou | 4 | 7 | 1.44 |
| Aimin Yang | 5 | 48 | 3.09 |
| Xinguang Li | 6 | 0 | 0.34 |