A Joint Segmentation and Classification Framework for Sentiment Analysis

Duyu Tang, Furu Wei, Bing Qin, Li Dong, Ting Liu, Ming Zhou

Abstract: In this paper, we propose a joint segmentation and classification framework for sentiment analysis. Existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as “not bad” and “a great deal of ”. We address this issue by developing a joint segmentation and classification framework (JSC), which simultaneously conducts sentence segmentation and sentence-level sentiment classification. Specifically, we use a log-linear model to score each segmentation candidate, and exploit the phrasal information of top-ranked segmentations as features to build the sentiment classifier. A marginal log-likelihood objective function is devised for the segmentation model, which is optimized for enhancing the sentiment classification performance. The joint model is trained only based on the annotated sentiment polarity of sentences, without any segmentation annotations. Experiments on a benchmark Twitter sentiment classification dataset in SemEval 2013 show that, our joint model performs comparably with the state-of-the-art methods.