Sciweavers

Share
ACL
2012

Cross-Domain Co-Extraction of Sentiment and Topic Lexicons

7 years 9 months ago
Cross-Domain Co-Extraction of Sentiment and Topic Lexicons
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.
Fangtao Li, Sinno Jialin Pan, Ou Jin, Qiang Yang,
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Fangtao Li, Sinno Jialin Pan, Ou Jin, Qiang Yang, Xiaoyan Zhu
Comments (0)
books