Learning discriminative sentiment chunk vectors for twitter sentiment Analysis

Journal of Internet Technology

Volumn 18, Issue 7, 2017, Pages 1605-1613

  Yan, Leiming ^a   Zheng, Wenying ^a   Zhang, Huajie ^b   Tao, Hao ^b   He, Ming ^c,d  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

^c PLA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d PLA Army University of Engineering   (China)

Author keywords

Bag Of Words; Deep Learning; Sentiment Analysis; Word Embedding

Indexed keywords

DATA MINING; DEEP LEARNING; SOCIAL NETWORKING (ONLINE); SUPERVISED LEARNING; VECTORS;

BAG OF WORDS; CLASSIFICATION TASKS; EXTRACTING FEATURES; HIGH-DIMENSIONAL; SEMI-SUPERVISED METHOD; SENTENCE STRUCTURES; SENTIMENT ANALYSIS; WORD EMBEDDING;

VECTOR SPACES;

EID: 85041836461     PISSN: 16079264     EISSN: 20794029     Source Type: Journal    
DOI: 10.6138/JIT.2017.18.7.20170410     Document Type: Article

References (27)

1. 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, 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, 2013, pp. 1631-1642.
2. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado and J. Deen, Distributed Representations of Words and Phrases and Their Compositionality, 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, 2013, pp. 3111-3119.
3. R. Socher, C. C. Y. Lin, A. Y. Ng and C. D. Manning, Parsing Natural Scenes and Natural Language with Recursive Neural Networks, 28th International Conference on Machine Learning, Bellevue, WA, 2011, pp. 129-136.
4. P. Nakov, S. Rosenthal, S. Kiritchenko, S. M. Mohammad, Z. Kozareva and A. Ritter, Developing a Successful SemEval Task in Sentiment Analysis of Twitter and Other Social Media Texts, Language Resources and Evaluation, Vol. 50, No. 1, pp. 35-65, March, 2016.
5. B. Liu, Sentiment Analysis and Opinion Mining, Cambridge University Press, 2015
6. J. M. Chenlo and D. E. Losada, An Empirical Study of Sentence Features for Subjectivity and Polarity Classifi cation, Information Sciences, Vol. 280, No. 1 pp. 275-288, October, 2014.
7. C. Priyanka and D. Gupta, Identifying the Best Feature Combination for Sentiment Analysis of Customer Reviews, 2013 International Conference on Advances in Computing, Communications and Informatics, Mysore, India, 2013, pp. 102-108.
8. E. Kouloumpis, T. Wilson and J. Moore, Twitter Sentiment Analysis: The Good the Bad and the OMG!, Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2011, pp. 538-541.
9. S. Sun, H. Liu, H. Lin and A. Abraham, Twitter Partof-speech Tagging Using Pre-classification Hidden Markov Model, 2012 IEEE International Conference on Systems, Man, and Cybernetics, Seoul, South Korea, 2012, pp. 1118-1123.
10. D. Tang, F. Wei, B. Qin, L. Dong, T. Liu and M. Zhou, A Joint Segmentation and Classification Framework for Sentiment Analysis, 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 477-487.
11. F. H. Khan, S. Bashir and U. Qamar, TOM: Twitter Opinion Mining Framework Using Hybrid Classification Scheme, Decision Support Systems, Vol. 57, pp. 245-257, January, 2014.
12. S. Baccianella, A. Esuli and F. Sebastiani, SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, Seventh Conference on International Language Resources and Evaluation, Valletta, Malta, 2010, pp. 2200-2204.
13. Y. Tsvetkov, M. Faruqui, W. Ling, G. Lample and C. Dyer, Evaluation of Word Vector Representations by Subspace Alignment, 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, pp. 2049-2054.
14. T. Mikolov, M. Karafiát, L. Burget, J. H. Cernocky and S. Khudanpur, Recurrent Neural Network Based Language Model, 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, 2010, pp. 1045-1048.
15. J. Cheng, X. Zhang, P. Li, S. Zhang, Z. Ding and H. Wang, Exploring Sentiment Parsing of Microblogging Texts for Opinion Polling on Chinese Public Figures, Applied Intelligence, Vol. 45, No. 2, pp. 429-442, September, 2016.
16. M. Sundermeyer, R. Schlüter and H. Ney, LSTM Neural Networks for Language Modeling, 13th Annual Conference of the International Speech Communication Association, Portland, OR, 2012, pp. 194-197.
17. D. Tang, F. Wei, B. Qin, T. Liu and M. Zhou, Coooolll: A Deep Learning System for Twitter Sentiment Classifi cation, 8th International Workshop on Semantic Evaluation, Dublin, Ireland, 2014, pp. 208-212.
18. J, Weston, F. Ratle and R. Collobert, Deep Learning via Semi-supervised Embedding, 25th International Conference on Machine Learning, Helsinki, Finland, 2008, pp. 1168-1175.
19. R. Socher, J. Pennington, E. H. Huang, A. Y. Ng and C. D. Manning, Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions, Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK, 2011, pp. 151-161.
20. H. Saif, M. Fernández, Y. He and H. Alani, Evaluation Datasets for Twitter Sentiment Analysis: A Survey and a New Dataset, the STS-gold, First International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI, Turin, Italy, 2013, pp. 9-21.
21. B. Gu, V. S. Sheng, Z. Wang, D. Ho, S. Osman and S. Li, Incremental Learning for ?-Support Vector Regression, Neural Networks, Vol. 67, No. C, pp. 140-150, July, 2015.
22. B. Gu, V. S. Sheng, K. Y. Tay, W. Romano and S. Li, Incremental Support Vector Learning for Ordinal Regression, IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 7, pp. 1403-1416, July, 2015.
23. B. Gu and V. S. Sheng, A Robust Regularization Path Algorithm for ?-Support Vector Classifi cation, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 5, pp. 1241-1248, May, 2017.
24. B. Gu, X. Sun and V. S. Sheng, Structural Minimax Probability Machine, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 7, pp. 1646-1656, July, 2017.
25. J. Shen, H. W. Tan, J. Wang, J. W. Wang and S. Y. Lee, A Novel Routing Protocol Providing Good Transmission Reliability in Underwater Sensor Networks, Journal of Internet Technology, Vol. 16, No. 1, pp. 171-178, January, 2015.
26. M. Speriosu, N. Sudan, S. Upadhyay and J. Baldridge, Twitter Polarity Classifi cation with Label Propagation over Lexical Links and the Follower Graph, EMNLP ?11 First Workshop on Unsupervised Learning in NLP, Edinburgh, Scotland, 2011, pp. 53-63.
27. M. Thelwall, K. Buckley and G. Paltoglou, Sentiment Strength Detection for the Social Web, Journal of the Society for Information Science and Technology, Vol. 63, No. 1, pp. 163-173, January, 2012.