Name
Abstract | ||
|---|---|---|
Extracting sentiment and topic lexicons is important for opinion mining. Previous works have showed that supervised learning methods are superior for this task. However, the performance of supervised methods highly relies on manually labeled training data. In this paper, we propose a domain adaptation framework for sentiment- and topic- lexicon co-extraction in a domain of interest where we do not require any labeled data, but have lots of labeled data in another related domain. The framework is twofold. In the first step, we generate a few high-confidence sentiment and topic seeds in the target domain. In the second step, we propose a novel Relational Adaptive bootstraPping (RAP) algorithm to expand the seeds in the target domain by exploiting the labeled source domain data and the relationships between topic and sentiment words. Experimental results show that our domain adaptation framework can extract precise lexicons in the target domain without any annotation. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | training data,target domain,related domain,topic lexicon,topic seed,extracting sentiment,source domain data,sentiment word,cross-domain co-extraction,domain adaptation framework,high-confidence sentiment |
Field | DocType | Volume |
Training set,Annotation,Sentiment analysis,Bootstrapping,Computer science,Domain adaptation,Supervised learning,Lexicon,Natural language processing,Artificial intelligence,Labeled data,Machine learning | Conference | P12-1 |
Citations | PageRank | References |
34 | 1.07 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangtao Li | 1 | 466 | 20.18 |
| Sinno Jialin Pan | 2 | 3128 | 122.59 |
| Ou Jin | 3 | 34 | 1.07 |
| Qiang Yang | 4 | 17039 | 875.69 |
| Xiaoyan Zhu | 5 | 2125 | 141.16 |