Paper: A Joint Segmentation and Classification Framework for Sentiment Analysis

ACL ID D14-1054
Title A Joint Segmentation and Classification Framework for Sentiment Analysis
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper, we propose a joint segmenta- tion and classification framework for sen- timent analysis. Existing sentiment clas- sification algorithms typically split a sen- tence as a word sequence, which does not effectively handle the inconsistent senti- ment 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 si- multaneously conducts sentence segmen- tation and sentence-level sentiment classi- fication. Specifically, we use a log-linear model to score each segmentation candi- date, and exploit the phrasal information of top-ranked segmentations as features to build the sentiment classifier. A marginal log-likelihood objective function is de- vis...