Paper: Cross-Domain Co-Extraction of Sentiment and Topic Lexicons

ACL ID P12-1043
Title Cross-Domain Co-Extraction of Sentiment and Topic Lexicons
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

Extracting sentiment and topic lexicons is im- portant for opinion mining. Previous works have showed that supervised learning methods are superior for this task. However, the perfor- mance 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 re- quire any labeled data, but have lots of labeled data in another related domain. The frame- work is twofold. In the first step, we gener- ate 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 ...