A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification

A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification

Abstract— A joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial< Final Year Projects 2016 > critical for sentiment classification. However, 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,” “bad”} and {“a great deal of,” “great”}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence; a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification; and a classification model for predicting the sentiment polarity of a segmentation.