This paper presents a weakly-supervised method for Chinese sentiment analysis by incorporating lexical prior knowledge obtained from English sentiment lexicons through machine translation. A mechanism is introduced to incorporate the prior information about polaritybearing words obtained from existing sentiment lexicons into latent Dirichlet allocation (LDA) where sentiment labels are considered as topics. Experiments on Chinese product reviews on mobile phones, digital cameras, MP3 players, and monitors demonstrate the feasibility and effectiveness of the proposed approach and show that the weakly supervised LDA model performs as well as supervised classifiers such as Naive Bayes and Support vector Machines with an average of 83% accuracy achieved over a total of 5484 review documents. Moreover, the LDA model is able to extract highly domain-salient polarity words from text.
REF used machine translation to learn lexical prior knowledge of English sentiment lexicons and incorporated the prior knowledge into latent Dirichlet allocation (LDA), where sentiment labels are considered as topics for sentiment analysis.
Exploring English Lexicon Knowledge for Chinese Sentiment Analysis
Proceedings of the Joint Conference on Chinese Language Processing
Computer Science