Exploiting Combined Multi-level Model for Document Sentiment Analysis

This paper focuses on the task of text sentiment analysis in hybrid online articles and web pages. Traditional approaches of text sentiment analysis typically work at a particular level, such as phrase, sentence or document level, which might not be suitable for the documents with too few or too many words. Considering every level analysis has its own advantages, we expect that a combination model may achieve better performance. In this paper, a novel combined model based on phrase and sentence level´s analyses and a discussion on the complementation of different levels´ analyses are presented. For the phrase-level sentiment analysis, a newly defined Left-Middle-Right template and the Conditional Random Fields are used to extract the sentiment words. The Maximum Entropy model is used in the sentence-level sentiment analysis. The experiment results verify that the combination model with specific combination of features is better than single level model.