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• 54 creator jian-tao-sun.
• 54 creator qiang-yang.
• 54 creator sinno-pan.
• 54 creator xiaochuan-ni.
• 54 creator zheng-chen.
• 54 type InProceedings.
• 54 label "Cross-Domain Sentiment Classification via Spectral Feature Alignment".
• 54 sameAs 54.
• 54 abstract "User sentiment data are widely spread on the Web. However, when users publish sentiment data (e.g., reviews, blogs), they may not explicitly indicate their sentiment polarity (e.g., positive or negative). Sentiment classification, which is used to automatically predict sentiment category, is critical in many applications particularly when user text is large in scale. Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. In this work, we develop a general solution for sentiment classification when we do not have any labels in a target domain. We solve this problem by leveraging the knowledge from a different domain with labels, which is what we call a source domain. In this cross-domain sentiment classification setting, if we directly apply a classification model trained in the source domain to the target domain, performance will be very low due to the differences between these domains. To bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align the domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. In this way, the clusters can be used to reduce the gap between domain specific words of the two domains, which can be used to train sentiment classifiers in the target domain accurately. Compared to previous approaches to cross-domain sentiment classification, SFA is both general and optimized, since it discovers a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domainindependent words. We perform extensive experiments on two real world datasets, and demonstrate that SFA outperforms previous approaches for cross-domain sentiment classification.".
• 54 hasAuthorList authorList.
• 54 isPartOf proceedings.
• 54 keyword "Opinion mining".
• 54 keyword "sentiment analysis".
• 54 title "Cross-Domain Sentiment Classification via Spectral Feature Alignment".