emnlp emnlp2010 emnlp2010-83 emnlp2010-83-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ainur Yessenalina ; Yisong Yue ; Claire Cardie
Abstract: 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., subjec- tive) 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.
Mohit Bansal, Claire Cardie, and Lillian Lee. 2008. The power of negative thinking: Exploiting label disagreement in the min-cut classification framework. In International Conference on Computational Linguistics (COLING). Ming-Wei Chang, Dan Goldwasser, Dan Roth, and Vivek Srikumar. 2010. Discriminative learning over constrained latent representations. In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). Yejin Choi and Claire Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Empirical Methods in Natural Language Processing (EMNLP). James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010. Driving semantic parsing from the world’s response. In ACL Conference on Natural Language Learning (CoNLL), July. Pedro Felzenszwalb, David McAllester, and Deva Ramanan. 2008. A discriminatively trained, multiscale, deformable part model. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Thomas Finley and Thorsten Joachims. 2008. Training structural svms when exact inference is intractable. In International Conference on Machine Learning (ICML). Thorsten Joachims, Thomas Finley, and Chun-Nam Yu. 2009. Cutting plane training of structural svms. Machine Learning, 77(1):27–59. Yi Mao and Guy Lebanon. 2006. Isotonic conditional random fields and local sentiment flow. In Neural Information Processing Systems (NIPS). Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Annual Meeting of the Association for Computational Linguistics (ACL). Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classification using crfs with hidden variables. In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Annual Meeting of the Association for Computational Linguistics (ACL). Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2): 1–135. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Empirical Methods in Natural Language Processing (EMNLP). 1056 Slav Petrov and Dan Klein. 2007. Discriminative loglinear grammars with latent variables. In Neural Information Processing Systems (NIPS). Matt Thomas, Bo Pang, and Lillian Lee. 2006. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Empirical Methods in Natural Language Processing (EMNLP). Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Empirical Methods in Natural Language Processing (EMNLP). Ainur Yessenalina, Yejin Choi, and Claire Cardie. 2010. Automatically generating annotator rationales to improve sentiment classification. In Annual Meeting of the Association for Computational Linguistics (ACL). Chun-Nam Yu and Thorsten Joachims. 2009. Learning structural svms with latent variables. In International Conference on Machine Learning (ICML). Alan L. Yuille and Anand Rangarajan. 2003. The concave-convex procedure. Neural Computation, 15(4):915–936, April. Omar F. Zaidan and Jason Eisner. 2008. Modeling annotators: a generative approach to learning from annotator rationales. In Empirical Methods in Natural Language Processing (EMNLP). Omar F. Zaidan, Jason Eisner, and Christine Piatko. 2007. Using “annotator rationales” to improve ma- chine learning for text categorization. In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).