Indexed by:Journal Papers

Date of Publication:2017-01-01

Journal:IAENG International Journal of Computer Science

Included Journals:Scopus、EI

Volume:44

Issue:2

Page Number:172-179

Abstract:Cross-domain sentiment classification trains robust classifiers across domains with the help of source domain labeled data. Sentiment is expressed differently in different domains. Sentiment terms that occur in a source domain may not appear in a different target domain. Such feature mismatches hinder cross-domain sentiment classification. Previous studies have addressed this problem by constructing a common feature representation or subspace; however, they have not considered semantic correlations between features. In this paper, we propose a cross-domain semantic correlation auto-correspondence method (CSCW) by capturing similar semantic features from different domains. First, our method uses sentiment-invariance words as features by considering their properties as strong sentiment indicators and their invariance across domains. Second, we extracted the top-N pivot features using a common frequency among source and target domains. These pivot features can then be employed to find semantically similar sentiment features from both domains. Third, for each pivot feature, by calculating the semantic similarity between non-pivot features and pivot features from either domain with the help of Word2Vec, we construct similar-pivot feature pairs that express similar sentiments but in different representations in either domain. Finally, we transform these pairs to align similar sentiment feature representations. This process avoids feature mismatches and reduces sentiment discrepancies between domains. The experimental results from testing on 12 source-target domain pairs of Amazon product reviews demonstrate that our method significantly outperforms previous approaches in sentiment classification.