Deep convolutional neural networks for sentiment analysis of short texts

COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers

Volumn , Issue , 2014, Pages 69-78

  Dos Santos, Cícero Nogueira ^a   Gatti, Maíra ^a  

^a IBM RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DATA MINING; LINGUISTICS; NEURAL NETWORKS; SOCIAL NETWORKING (ONLINE);

CONTEXTUAL INFORMATION; CONVOLUTIONAL NEURAL NETWORK; DIFFERENT DOMAINS; POSITIVE/NEGATIVE CLASSIFICATIONS; PREDICTION ACCURACY; PRIOR KNOWLEDGE; SENTIMENT ANALYSIS; STATE OF THE ART;

COMPUTATIONAL LINGUISTICS;

EID: 84932166511     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Andrei Alexandrescu and Katrin Kirchhoff. 2006. Factored neural language models. In Proceedings of the Human Language Technology Conference of the NAACL, pages 1-4, New York City, USA, June.
2. Luciano Barbosa and Junlan Feng. 2010. Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 36-44.
3. James Bergstra, Olivier Breuleux, Fŕederic Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. 2010. Theano: A CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy).
4. Grzegorz Chrupala. 2013. Text segmentation with character-level text embeddings. In Proceedings of the ICML workshop on Deep Learning for Audio, Speech and Language Processing.
5. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493-2537.
6. R. Collobert. 2011. Deep learning for efficient discriminative parsing. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pages 224-232.
7. Ćicero Nogueira dos Santos and Bianca Zadrozny. 2014. Learning character-level representations for part-ofspeech tagging. In Proceedings of the 31st International Conference on Machine Learning, JMLR: W&CP volume 32, Beijing, China.
8. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. Technical report, Stanford University.
9. Angeliki Lazaridou, Marco Marelli, Roberto Zamparelli, and Marco Baroni. 2013. Compositional-ly derived representations of morphologically complex words in distributional semantics. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), pages 1517-1526.
10. Yann Lecun, Lon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324.
11. Minh-Thang Luong, Richard Socher, and Christopher D. Manning. 2013. Better word representations with recursive neural networks for morphology. In Proceedings of the Conference on Computational Natural Language Learning, Sofia, Bulgaria.
12. Christopher D. Manning. 2011. Part-of-speech tagging from 97% to 100%: Is it time for some linguistics? In Proceedings of the 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing'11, pages 171-189.
13. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of Workshop at International Conference on Learning Representations.
14. Preslav Nakov, Sara Rosenthal, Zornitsa Kozareva, Veselin Stoyanov, Alan Ritter, and Theresa Wilson. 2013.Semeval-2013 task 2: Sentiment analysis in twitter. In Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 312-320, Atlanta, Georgia, USA, June. Association for Computational Linguistics.
15. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semisupervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 151-161.
16. Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of theConference on Empirical Methods in Natural Language Processing, pages 1201-1211.
17. Richard Socher, John Bauer, Christopher D. Manning, and Andrew Y. Ng. 2013a. Parsing with compositional vector grammars. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
18. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013b. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1631-1642.
19. Michael Speriosu, Nikita Sudan, Sid Upadhyay, and Jason Baldridge. 2011. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the FirstWorkshop on Unsupervised Learning in NLP, EMNLP, pages 53-63.
20. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. J. Lang. 1989. Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing, 37(3):328-339.
21. Xiaoqing Zheng, Hanyang Chen, and Tianyu Xu. 2013. Deep learning for Chinese word segmentation and pos tagging. In Proceedings of the Conference on Empirical Methods in NLP, pages 647-657.