Sciweavers

Share
WWW
2010
ACM

Cross-domain sentiment classification via spectral feature alignment

10 years 3 months ago
Cross-domain sentiment classification via spectral feature alignment
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). 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. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain. In this cross-domain sentiment classification setting, to bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into ...
Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qian
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where WWW
Authors Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, Zheng Chen
Comments (0)
books