A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification

IEEE Transactions on Audio, Speech and Language Processing

Volumn 23, Issue 11, 2015, Pages 1750-1761

  Tang, Duyu ^a   Qin, Bing ^a   Wei, Furu ^b   Dong, Li ^c   Liu, Ting ^a   Zhou, Ming ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b MICROSOFT RESEARCH   (United States)

^c BEIHANG UNIVERSITY   (China)

Author keywords

Artificial intelligence; joint segmentation and classification; natural language processing; sentiment analysis; sentiment classification

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

CLASSIFICATION FRAMEWORK; CLASSIFICATION MODELS; JOINT SEGMENTATION; NATURAL LANGUAGE PROCESSING; SEGMENTATION RESULTS; SENTIMENT ANALYSIS; SENTIMENT CLASSIFICATION; STATE-OF-THE-ART METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 84936797525     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASLP.2015.2449071     Document Type: Article

References (65)

1. B. Pang and L. Lee,"Opinion mining and sentiment analysis" Foundations Trends Inf. Retriev., vol. 2, no. 1-2, pp. 1-135, 2008
2. B. Liu,"Sentiment analysis and opinion mining" Synth. Lectures Human Lang. Technol., vol. 5, no. 1, pp. 1-167, 2012
3. C. Havasi, E. Cambria, B. Schuller, B. Liu, and H. Wang,"Knowledge-based approaches to concept-level sentiment analysis" IEEE Intell. Syst., vol. 28, no. 2, pp. 0012-14, Mar.-Apr. 2013
4. C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 1999
5. P. D. Turney,"Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews" in Proc. ACL, 2002, pp. 417-424
6. M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede,"Lexiconbased methods for sentiment analysis" Comput. linguist., vol. 37, no. 2, pp. 267-307, 2011
7. B. Pang, L. Lee, and S. Vaithyanathan,"Thumbs up: Sentiment classification using machine learning techniques" in Proc. EMNLP, 2002, pp. 79-86
8. J. Zhao, L. Dong, J. Wu, and K. Xu,"Moodlens: An emoticon-based sentiment analysis system for chinese tweets" in Proc. SIGKDD, 2012
9. A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts,"Learning word vectors for sentiment analysis" in Proc. ACL, 2011
10. G. Paltoglou and M. Thelwall,"A study of information retrieval weighting schemes for sentiment analysis" in Proc. ACL, 2010, pp. 1386-1395
11. Y. Choi and C. Cardie,"Learning with compositional semantics as structural inference for subsentential sentiment analysis" in Proc. EMNLP, 2008, pp. 793-801
12. S. M. Mohammad, B. J. Dorr, G. Hirst, and P. D. Turney,"Computing lexical contrast" Comput. Linguist., vol. 39, no. 3, pp. 555-590, 2013
13. P. Nakov, S. Rosenthal, Z. Kozareva, V. Stoyanov, A. Ritter, and T. Wilson,"Semeval-2013 task 2: Sentiment analysis in twitter" in Proc. Int. Workshop Semant. Eval., 2013, vol. 13
14. B. Pang and L. Lee,"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales" in ACL Assoc. Comput. Linguist., 2005, pp. 115-124
15. D. Tang, F. Wei, B. Qin, L. Dong, T. Liu, and M. Zhou,"A joint segmentation and classification framework for sentiment analysis" in Proc. EMNLP, 2014, pp. 477-487, Assoc. for Comput. Linguist
16. X. Ding, B. Liu, and P. S. Yu,"A holistic lexicon-based approach to opinion mining" in Proc. Int. Conf. Web Search and Data Mining, 2008, pp. 231-240
17. M. Thelwall, K. Buckley, and G. Paltoglou,"Sentiment strength detection for the social web" J. Amer. Soc. Inf. Sci. Technol., vol. 63, no. 1, pp. 163-173, 2012
18. H. Reckman, C. Baird, J. Crawford, R. Crowell, L. Micciulla, S. Sethi, and F. Veress,"Teragram: Rule-based detection of sentiment phrases using SAS sentiment analysis" in Proc. 7th Int. Workshop Semant. Eval. (SemEval'13), 2013, pp. 513-519
19. S. Wang and C. D. Manning,"Baselines and bigrams: Simple, good sentiment and topic classification" in Proc. ACL, 2012, pp. 90-94
20. H. D. Kim and C. Zhai,"Generating comparative summaries of contradictory opinions in text" in Proc. CIKM'09, 2009, ACM
21. T. Nakagawa, K. Inui, and S. Kurohashi,"Dependency tree-based sentiment classification using CRFS with hidden variables" in Proc. NAACL, 2010, pp. 786-794
22. S. M. Mohammad, S. Kiritchenko, and X. Zhu,"NRC-Canada: Building the state-of-The-art in sentiment analysis of tweets" in Proc. Int. Workshop Semant. Eval., 2013
23. A. Go, R. Bhayani, and L. Huang,"Twitter sentiment classification using distant supervision" Stanford Univ., Stanford, CA, USA, CS224N Project Rep., 2009, pp. 1-12
24. Y. Zhang, H. Zhang, M. Zhang, Y. Liu, and S. Ma,"Do users rate or review?: Boost phrase-level sentiment labeling with review-level sentiment classification" in Proc. SIGIR, 2014, pp. 1027-1030, ACM
25. Y. Bengio, A. Courville, and P. Vincent,"Representation learning: A review and new perspectives" IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013
26. N. Jones,"Computer science: The learning machines" Nature, vol. 505, no. 7482, p. 146, 2014
27. J. Pennington, R. Socher, and C. Manning,"Glove: Global vectors for word representation" in Proc. EMNLP, 2014, pp. 1532-1543
28. J. Mitchell and M. Lapata,"Composition in distributional models of semantics" Cognitive Sci., vol. 34, no. 8, pp. 1388-1429, 2010
29. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean,"Distributed representations of words and phrases and their compositionality" in Proc. Conf. Neural Inf. Process. Syst., 2013
30. O. Levy and Y. Goldberg,"Dependency-based word embeddings" in Proc. ACL, 2014, pp. 302-308, Assoc. for Comput. Linguist
31. J. Li and D. Jurafsky,"Do multi-sense embeddings improve natural language understanding" arXiv preprint arXiv:1506.01070, 2015
32. D. M. Blei, A. Y. Ng, and M. I. Jordan,"Latent Dirichlet allocation" J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003
33. I. Labutov and H. Lipson,"Re-embedding words" in Proc. Annu. Meeting Assoc. Comput. Linguist., 2013
34. D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin,"Learning sentiment-specific word embedding for twitter sentiment classification" in Proc. ACL, 2014, pp. 1555-1565
35. D. Tang, F. Wei, B. Qin, M. Zhou, and T. Liu,"Building large-scale twitter-specific sentiment lexicon: A representation learning approach" in Proc. COLING, 2014, pp. 172-182
36. X. Glorot, A. Bordes, and Y. Bengio,"Domain adaptation for largescale sentiment classification: A deep learning approach" in Proc. Int. Conf. Mach. Learn., 2011
37. R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts,"Recursive deep models for semantic compositionality over a sentiment treebank" in Proc. EMNLP, 2013, pp. 1631-1642
38. J. Li,"Feature weight tuning for recursive neural networks" Arxiv preprint 1412.3714, 2014
39. N. Kalchbrenner, E. Grefenstette, and P. Blunsom,"A sentence model based on convolutional neural networks" in Proc. ACL, 2014
40. Q. Le and T. Mikolov,"Distributed representations of sentences and documents" in Proc. ICML, 2014
41. J. Li, D. Jurafsky, and E. Hovy,"When are tree structures necessary for deep learning of representations?" arXiv preprint:1503.00185, 2015
42. D. Tang, B. Qin, T. Liu, and Y. Yang,"User modeling with neural network for review rating prediction" in Proc. IJCAI, 2015
43. D. Tang, B. Qin, and T. Liu,"Learning semantic representations of users and products for document level sentiment classification" in Proc. ACL, 2015
44. B. Pang and L. Lee,"A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts" in Proc. ACL, 2004
45. Y. Mao and G. Lebanon,"Isotonic conditional random fields and local sentiment flow" in Proc. NIPS, 2006, pp. 961-968
46. O. Zaidan, J. Eisner, and C. D. Piatko,"Using" annotator rationales" to improve machine learning for text categorization" in Proc. HLTNAACL, 2007, pp. 260-267
47. A. Yessenalina, Y. Yue, and C. Cardie,"Multi-level structured models for document-level sentiment classification" in Proc. EMNLP, 2010, pp. 1046-1056
48. R. McDonald, K. Hannan, T. Neylon, M. Wells, and J. Reynar,"Structured models for fine-To-coarse sentiment analysis" in Proc. ACL, 2007
49. O. F. Zaidan and J. Eisner,"Modeling annotators: A generative approach to learning from annotator rationales" in Proc. EMNLP, 2008, pp. 31-40
50. M. Hu and B. Liu,"Mining and summarizing customer reviews" in Proc. SIGKDD, 2004, pp. 168-177
51. L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu,"Adaptive recursive neural network for target-dependent twitter sentiment classification" in Proc. ACL, 2014, pp. 49-54
52. J. Li and E. Hovy,"Sentiment analysis on the peoples daily" in Proc. EMNLP, 2014
53. D. C. Liu and J. Nocedal,"On the limited memory BFGS method for large scale optimization" Math. Program., vol. 45, no. 1-3, pp. 503-528, 1989
54. J. Zhang, S. Liu, M. Li, M. Zhou, and C. Zong,"Bilingually-constrained phrase embeddings for machine translation" in Proc. ACL, 2014, pp. 111-121
55. K. Gimpel, N. Schneider, B. O'Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith,"Partof-speech tagging for twitter: Annotation, features, and experiments" in Proc. ACL, 2011, pp. 42-47
56. T. Wilson, J. Wiebe, and P. Hoffmann,"Recognizing contextual polarity in phrase-level sentiment analysis" in Proc. EMNLP, 2005, pp. 347-354
57. S. M. Mohammad and P. D. Turney,"Crowdsourcing a word-emotion association lexicon" Comput. Intell., vol. 29, no. 3, pp. 436-465, 2012
58. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin,"Liblinear: A library for large linear classification" J. Mach. Learn. Res., vol. 9, pp. 1871-1874, 2008
59. X. Hu, J. Tang, H. Gao, and H. Liu,"Unsupervised sentiment analysis with emotional signals" in Proc. WWW, 2013, pp. 607-618
60. R. Socher, J. Pennington, E. Huang, A. Ng, and C. Manning,"Semisupervised recursive autoencoders for predicting sentiment distributions" in Proc. EMNLP, 2011, pp. 151-161
61. D. Tang, F. Wei, B. Qin, T. Liu, and M. Zhou,"Coooolll: A deep learning system for twitter sentiment classification" in Proc. 8th Int. Workshop Semant. Eval., 2014, pp. 208-212
62. R. Socher, B. Huval, C. D. Manning, and A. Y. Ng,"Semantic compositionality through recursive matrix-vector spaces" in Proc. EMNLP, 2012
63. L. Dong, F. Wei, S. Liu, M. Zhou, and K. Xu,"A statistical parsing framework for sentiment classification" arXiv preprint arXiv:1401. 6330, 2014
64. Y. Kim,"Convolutional neural networks for sentence classification" in Proc. EMNLP, 2014, pp. 1746-1751
65. G. Frege,"On sense and reference" in Ludlow (1997), 1892, pp. 563-584