Context-sensitive lexicon features for neural sentiment analysis

EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Volumn , Issue , 2016, Pages 1629-1638

  Teng, Zhiyang ^a   Vo, Duy Tin ^a   Zhang, Yue ^a  

^a SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

SENTIMENT ANALYSIS;

CONTEXT SENSITIVE; LEXICAL FEATURES; LEXICON-BASED; SENTIMENT LEXICONS; SENTIMENT SCORES; STATE OF THE ART; WEIGHTED SUM MODEL;

RECURRENT NEURAL NETWORKS;

EID: 85068453397     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d16-1169     Document Type: Conference Paper

References (52)

1. Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow, and Rebecca Passonneau. 2011. Sentiment analysis of twitter data. In Proceedings of the Workshop on Languages in Social Media, pages 30-38.
2. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of LREC, volume 10, pages 2200-2204.
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473.
4. Erik Cambria. 2016. Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2):102-107.
5. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of EMNLP, pages 1724-1734.
6. Yejin Choi and Claire Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of EMNLP, pages 793-801.
7. Kerstin Denecke. 2009. Are sentiwordnet scores suited for multi-domain sentiment classification? In ICDIM, pages 1-6. IEEE.
8. Ann Devitt and Khurshid Ahmad. 2007. Sentiment polarity identification in financial news: A cohesion-based approach.
9. Xiaowen Ding, Bing Liu, and Philip S Yu. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pages 231-240.
10. Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of ACL, pages 49-54.
11. Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015. Transition-based dependency parsing with stack long short-term memory. In ACL.
12. Andrea Esuli and Fabrizio Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC, volume 6, pages 417-422.
13. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1:12.
14. Alex Graves and Jürgen Schmidhuber. 2005. Frame-wise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks, 18(5):602-610.
15. A. Graves, A. Mohamed, and G. Hinton. 2013. Speech recognition with deep recurrent neural networks.
16. Marco Guerini, Lorenzo Gatti, and Marco Turchi. 2013. Sentiment analysis: How to derive prior polarities from SentiWordNet. In Proceedings of EMNLP, pages 1259-1269.
17. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
18. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of KDD, KDD'04, pages 168-177.
19. Ozan Irsoy and Claire Cardie. 2014. Deep recursive neural networks for compositionality in language. In Advances in Neural Information Processing Systems, pages 2096-2104.
20. Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modelling sentences. In Proceedings of ACL, pages 655-665.
21. Colm Kearney and Sha Liu. 2014. Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33:171-185.
22. Soo-Min Kim and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of the 20th international conference on Computational Linguistics, page 1367.
23. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of EMNLP, pages 1746-1751.
24. Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014. Sentiment analysis of short informal texts. J. Artif. Intell. Res. (JAIR), 50:723-762.
25. Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. CoRR, abs/1405.4053.
26. Phong Le and Willem Zuidema. 2015. Compositional distributional semantics with long short term memory. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, pages 10-19.
27. Jiwei Li, Minh-Thang Luong, Dan Jurafsky, and Eudard Hovy. 2015. When are tree structures necessary for deep learning of representations?
28. Pengfei Liu, Xipeng Qiu, Xinchi Chen, Shiyu Wu, and Xuanjing Huang. 2015. Multi-timescale long short-term memory neural network for modelling sentences and documents. In Proceedings of EMNLP, pages 2326-2335.
29. Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013. Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of SemEval-2013, June.
30. Karo Moilanen and Stephen Pulman. 2007. Sentiment composition.
31. Richard Montague. 1974. Formal Philosophy: Selected Papers of Richard Montague. Ed. and with an Introd. by Richmond H. Thomason. Yale University Press.
32. 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 SemEval-2013, pages 312-320.
33. Thien Hai Nguyen and Kiyoaki Shirai. 2015. Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis. In Proceedings of EMNLP.
34. Livia Polanyi and Annie Zaenen. 2006. Contextual valence shifters. In Computing attitude and affect in text: Theory and applications, pages 1-10.
35. Yafeng Ren, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016. Context-sensitive twitter sentiment classification using neural network. In Proceedings of AAAI.
36. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. In Proceedings of EMNLP.
37. Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of EMNLP, pages 1201-1211.
38. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, D. Christopher Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP, pages 1631-1642.
39. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958.
40. Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th international conference on machine learning (ICML-13), pages 1139-1147.
41. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2):267-307.
42. Oscar Täckström and Ryan McDonald. 2011. Discovering fine-grained sentiment with latent variable structured prediction models. In Advances in Information Retrieval, pages 368-374.
43. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of ACL, pages 1556-1566.
44. Duyu Tang, Furu Wei, Bing Qin, Ming Zhou, and Ting Liu. 2014a. Building large-scale twitter-specific sentiment lexicon: A representation learning approach. In Proceedings of COLING, pages 172-182, August.
45. Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. 2014b. Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of ACL, pages 1555-1565, June.
46. Peter Turney. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL.
47. Duy-Tin Vo and Yue Zhang. 2015. Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of IJCAI, pages 1347-1353, July.
48. Xin Wang, Yuanchao Liu, Chengjie SUN, Baoxun Wang, and Xiaolong Wang. 2015. Predicting polarities of tweets by composing word embeddings with long short-term memory. In Proceedings of ACL, pages 1343-1353.
49. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT-EMNLP.
50. Meishan Zhang, Yue Zhang, and Duy-Tin Vo. 2016. Gated neural networks for targeted sentiment analysis.
51. Xiaodan Zhu, Svetlana Kiritchenko, and Saif M Mohammad. 2014. Nrc-canada-2014: Recent improvements in the sentiment analysis of tweets.
52. Xiaodan Zhu, Parinaz Sobhani, and Hongyu Guo. 2015. Long short-term memory over tree structures. CoRR, abs/1503.04881.