Name
Abstract | ||
|---|---|---|
Sentiment classification is attracting more and more attention because of its great benefits to social and human life. Usually supervised classification approaches perform well in sentiment classification, but the performance decreases sharply when transferred from one domain to another domain. In this paper, we propose an approach, SentiRank, which integrates the sentiment orientations of the documents into the graph-ranking algorithm for cross-domain sentiment classification. We apply the graph-ranking algorithm using the accurate labels of old-domain documents as well as the “pseudo” labels of new-domain documents, and investigate their relative importance for cross-domain sentiment classification. The experiment results indicate that the proposed algorithm could improve the performance of cross-domain sentiment classification dramatically. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WI-IAT.2009.55 | Web Intelligence |
Keywords | Field | DocType |
speaker recognition,intelligent agent,face recognition,testing,multimedia,video,web pages,social network,training data | Training set,Data mining,Graph,Facial recognition system,Intelligent agent,Web page,Ranking,Sentiment analysis,Computer science,Speaker recognition,Artificial intelligence,Machine learning | Conference |
Volume | Issue | Citations |
1 | null | 8 |
PageRank | References | Authors |
0.52 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiong Wu | 1 | 11 | 0.93 |
| Songbo Tan | 2 | 886 | 43.35 |
| Haijun Zhai | 3 | 62 | 7.40 |
| Gang Zhang | 4 | 8 | 0.52 |
| Miyi Duan | 5 | 19 | 2.84 |
| Xueqi Cheng | 6 | 3148 | 247.04 |