A Smart Sentiment Analysis System in Word, Sentence and Text Level

Abstract. Recently, sentiment analysis of text is becoming a hotspot in the study of natural language processing, which has drawn interesting attention due to its research value and extensive applications. This paper introduces a smart sentiment analysis system, which is to satisfy three aspects of sentiment analysis requirement. These are Chinese sentiment word recognition and analysis, sentiment related element extraction and text orientation analysis. Promising results and analysis are presented at the end of this paper

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text