Name
Abstract | ||
|---|---|---|
Analyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domainspecific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified cotraining framework. It is capable of incorporating both domain-specific and domainindependent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-ofthe-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese. |
Year | Venue | Field |
|---|---|---|
2015 | HLT-NAACL | Computer science,Common knowledge,Exploit,Lexicon,Natural language processing,Artificial intelligence,Labeled data,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.35 |
References | Authors | |
20 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Yang | 1 | 31 | 2.63 |
| Wenting Tu | 2 | 85 | 9.48 |
| Ziyu Lu | 3 | 40 | 7.14 |
| Wenpeng Yin | 4 | 387 | 23.87 |
| Kam-Pui Chow | 5 | 26 | 6.10 |