Name
Abstract | ||
|---|---|---|
This work proposes a semi-sentiment classification method by exploiting co-occurrence opinion words. Our method is based on the observation that opinion words with similar sentiment have high possibility to co-occur with each other. We show co-occurrence opinion words are helpful for improving sentiment classification accuracy. We employ the co-training framework to conduct semi-supervised sentiment classification. Experimental results show that our proposed method has better performance than the Self-learning SVM method. © Springer-Verlag 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-53914-5_4 | ADMA (1) |
Field | DocType | Volume |
Data mining,Sentiment analysis,Computer science,Support vector machine,Computational linguistics,Co-occurrence,Artificial intelligence,Natural language processing,Machine learning | Conference | 8346 LNAI |
Issue | ISSN | Citations |
PART 1 | 16113349 | 1 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suke Li | 1 | 15 | 4.37 |
| Jinmei Hao | 2 | 10 | 2.20 |
| Yanbing Jiang | 3 | 40 | 6.00 |
| Qi Jing | 4 | 1 | 0.68 |