Name
Abstract | ||
|---|---|---|
Target-dependent sentiment analysis on Twitter is the problem of identifying the sentiment polarity towards a certain target in a given tweet. All the existing studies of this task assume that the target is known. However, in such tasks, extracting the targets from the text is one of the most important subtasks. In this paper, we propose a model based on Bidirectional Gated Recurrent Units and Conditional Random Fields to identify automatically the targets from the tweets. The model has been evaluated on two benchmarks of tweets, obtaining results which show its superiority over several baseline methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-806-8-66 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Twitter,Target Identification,Text Mining,Recurrent Neural Networks,Conditional Random Fields | Conditional random field,Pattern recognition,Computer science,Speech recognition,Artificial intelligence | Conference |
Volume | ISSN | Citations |
300 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Jabreel | 1 | 5 | 1.20 |
| Fadi Hassan | 2 | 1 | 0.70 |
| Saddam Abdulwahab | 3 | 0 | 0.34 |
| Antonio Moreno | 4 | 7 | 4.37 |