LAUSR

Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis.