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EMNLP
2010

Multi-Level Structured Models for Document-Level Sentiment Classification

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Multi-Level Structured Models for Document-Level Sentiment Classification
In this paper, we investigate structured models for document-level sentiment classification. When predicting the sentiment of a subjective document (e.g., as positive or negative), it is well known that not all sentences are equally discriminative or informative. But identifying the useful sentences automatically is itself a difficult learning problem. This paper proposes a joint two-level approach for document-level sentiment classification that simultaneously extracts useful (i.e., subjective) sentences and predicts document-level sentiment based on the extracted sentences. Unlike previous joint learning methods for the task, our approach (1) does not rely on gold standard sentence-level subjectivity annotations (which may be expensive to obtain), and (2) optimizes directly for document-level performance. Empirical evaluations on movie reviews and U.S. Congressional floor debates show improved performance over previous approaches.
Ainur Yessenalina, Yisong Yue, Claire Cardie
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors Ainur Yessenalina, Yisong Yue, Claire Cardie
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