A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification
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A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification
Abstract? A joint segmentation and classi?cation framework for sentence-level sentiment classi?cation. It is widely recognized that phrasal information is crucial< Final Year Projects 2016 > critical for sentiment classi?cation. However, existing sentiment classi?cation 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 classi?cation. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Speci?cally, 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 classi?cation; and a classi?cation model for predicting the sentiment polarity of a segmentation.
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| Domains | Datamining |
|---|---|
| Programming Language | Java |
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