emnlp emnlp2011 emnlp2011-120 emnlp2011-120-reference knowledge-graph by maker-knowledge-mining
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Author: Richard Socher ; Jeffrey Pennington ; Eric H. Huang ; Andrew Y. Ng ; Christopher D. Manning
Abstract: We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment prediction tasks these representations outperform other state-of-the-art approaches on commonly used datasets, such as movie reviews, without using any pre-defined sentiment lexica or polarity shifting rules. We also evaluate the model’s ability to predict sentiment distributions on a new dataset based on confessions from the experience project. The dataset consists of personal user stories annotated with multiple labels which, when aggregated, form a multinomial distribution that captures emotional reactions. Our algorithm can more accurately predict distributions over such labels compared to several competitive baselines.
P. Beineke, T. Hastie, C. D. Manning, and S. Vaithyanathan. 2004. Exploring sentiment summarization. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. 2003. A neural probabilistic language model. Journal ofMachine Learning Research, 3: 1137–1 155. D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research., 3:993–1022. Y. Choi and C. Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In EMNLP. R. Collobert and J. Weston. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of ICML, pages 160–167. S. Das and M. Chen. 2001. Yahoo! for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia Pacific Finance Association Annual Conference (APFA). K. Dave, S. Lawrence, and D. M. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of WWW, pages 519–528. X. Ding, B. Liu, and P. S. Yu. 2008. A holistic lexiconbased approach to opinion mining. In Proceedings of the Conference on Web Search and Web Data Mining (WSDM). J. L. Elman. 1991. Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3): 195–225. A. Esuli and F. Sebastiani. 2007. Pageranking wordnet synsets: An application to opinion mining. In Proceedings of the Association for Computational Lin- guistics (ACL). C. Goller and A. K ¨uchler. 1996. Learning taskdependent distributed representations by backpropagation through structure. In Proceedings of the International Conference on Neural Networks (ICNN-96). G. Grefenstette, Y. Qu, J. G. Shanahan, and D. A. Evans. 2004. Coupling niche browsers and affect analysis for an opinion mining application. In Proceedings of Recherche d’Information Assist e´e par Ordinateur (RIAO). D. Ikeda, H. Takamura, L. Ratinov, and M. Okumura. 2008. Learning to shift the polarity of words for sentiment classification. In IJCNLP. S. Kim and E. Hovy. 2007. Crystal: Analyzing predictive opinions on the web. In EMNLP-CoNLL. A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. 2011. Learning accurate, compact, and interpretable tree annotation. In Proceedings of ACL. Y. Mao and G. Lebanon. 2007. Isotonic Conditional Random Fields and Local Sentiment Flow. In NIPS. P. Mirowski, M. Ranzato, and Y. LeCun. 2010. Dynamic auto-encoders for semantic indexing. In Proceedings of the NIPS 2010 Workshop on Deep Learning. T. Nakagawa, K. Inui, and S. Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In NAACL, HLT. B. Pang and L. Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL. 161 B. Pang and L. Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115–124. B. Pang and L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2): 1–135. B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In EMNLP. J. W. Pennebaker, R.J. Booth, and M. E. Francis. 2007. Linguistic inquiry and word count: Liwc2007 operators manual. University of Texas. L. Polanyi and A. Zaenen. 2006. Contextual valence shifters. J. B. Pollack. 1990. Recursive distributed representations. Artificial Intelligence, 46:77–105, November. C. Potts. 2010. On the negativity of negation. In David Lutz and Nan Li, editors, Proceedings of Semantics and Linguistic Theory 20. CLC Publications, Ithaca, NY. B. Snyder and R. Barzilay. 2007. Multiple aspect ranking using the Good Grief algorithm. In HLT-NAACL. R. Socher, C. D. Manning, and A. Y. Ng. 2010. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop. R. Socher, C. C. Lin, A. Y. Ng, and C. D. Manning. 2011. Parsing Natural Scenes and Natural Language with Recursive Neural Networks. In ICML. P. J. Stone. 1966. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press. J. Turian, L. Ratinov, and Y. Bengio. 2010. Word representations: a simple and general method for semisupervised learning. In Proceedings of ACL, pages 384–394. P. Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In ACL. L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R. McDonald. 2010. The viability of web-derived polarity lexicons. In NAACL, HLT. T. Voegtlin and P. Dominey. 2005. Linear Recursive Distributed Representations. Neural Networks, 18(7). J. Wiebe, T. Wilson, and C. Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39. T. Wilson, J. Wiebe, and P. Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT/EMNLP. H. Yu and V. Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In EMNLP.