基于子视图框架的语句级情感分析

his paper presents a method for improving the performance of sentence-level sentiment analysis. Sentiment analysis is thought to require a deep understanding of the sentence structure (e.g., word order and non-local dependency). To attack this problem without the sentence parsing, we propose an approach whereby a given sentence is decomposed into a series of sub-sequences or sub-view representations. Sentence-level polarity is then determined by classifying within sub-views and fusing the obtained sub-view polarities. Two specific methods are instantiated: stacking-based maximum entropy model and hidden conditional random fields (HCRFs) based on contextual features. Extensive evaluations were carried out on two benchmark dataset, one is for sentence subjectivity classification and the other is for sentence polarity detection. Experimental results show that the performance of our proposed method is comparable to the state-of-the-art approaches.