JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (9): 19-25.doi: 10.6040/j.issn.1671-9352.1.2016.PC4

Previous Articles     Next Articles

An emotional classification method based on joint deep learning model

YANG Yan, XU Bing*, YANG Mu-yun, ZHAO Jing-jing   

  1. Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150000, Heilongjiang, China
  • Received:2016-11-25 Online:2017-09-20 Published:2017-09-15

PDF (PC)

631

Abstract: According to the analysis of emotional problems in the modeling of long and short sentences different characteristics to sentiment classification, this paper proposes a classification algorithm based on the model of joint deep learning. Fusion of long short term memory model of the method(LSTM)and convolutional neural network(CNN)on film reviews emotional polarity discrimination, in the method, LSTM model was used to model context, through the word iteration to get feature vector context, and CNN model was used to automatically discover features from the word vector sequence, and integrating local features from the local feature extraction into global features to improve the classification results. The method proposed in this paper is to obtain the best results of the system accuracy in the task of the COAE2016 evaluation task 2.

Key words: sentiment classification, CNN, LSTM

CLC Number: 

  • TP391.1
[1] YU H, HATZIVASSILOGLOU V. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences[J]. Pediatrics, 2005, 116(3):58-59.
[2] KIM S M, HOVYE. Determining the sentiment of opinions[C] // Proceedings of the 20th International Conference on Computational Linguistics. Barcelona:Association for Computational Linguistics, 2004:1367.
[3] TURNEY PD. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews[C] //Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. USA: Association for Computational Linguistics, 2002: 417-424.
[4] HASSAN A, RADEVD. Identifying text polarity using randomwalks.[C] //Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Uppsala: Association for Computational Linguistics, 2010: 395-403.
[5] PANG B, LEE L, VAITHYANATHAN S. Thumbs up? sentiment classification using machine learning techniques.[C] //Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10. USA: Association for Computational Linguistics, 2002: 79-86.
[6] DAVIDOV D, TSUR O, RAPPOPORT A. Enhanced sentiment learning using twitter hashtags and smileys[C] // Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Uppsala:Association for Computational Linguistics, 2010: 241-249.
[7] WANG Xiaolong, WEI Furu, LIU Xiaohua, et al. Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach[C] //Proceedings of the 20th ACM International Conference on Information and knowledge Management. Scottsdale: ACM, 2011: 1031-1040.
[8] GONZALEZ-IBANEZ R, MURESAN S, WACHOLDER N. Identifying sarcasm in twitter: a closer look.[C] // Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2. Portland: Association for Computational Linguistics, 2011: 581-586.
[9] 谢丽星, 周明, 孙茂松. 基于层次结构的多策略中文微博情感分析和特征抽取[J]. 中文信息学报, 2012, 26(1): 73-83. XIE Lixing, ZHOU Ming, SUN Maosong. Hierarchical structure based hybrid approach to sentiment analysis of chinese micro blog and its feature extraction[J].Journal of Chinese Information Processing, 2012, 26(1): 73-83.
[10] SASHIHITHLU S, SOMANS S. Complex sentiment analysis using recursive autoencoders[J/OL].Uk: CiteSeerX, 2013.[2015-09-30]. https://core. ac. uk/display/23426251.
[11] SOCHER R, HUVAL B, MANNING C D, et al. Semantic compositionality throughrecursive matrix-vector spaces[C] //Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.USA:Association for Computational Linguistics, 2012: 1201-1211.
[12] SOCHER R, PERELYGIN A, WUJ Y, et al. Recursive deep models for semantic compositionality over a sentiment treebank[C] //Proceedings of the Conference on Empirical Methods in Natural Language Processing. Seattle:EMNLP, 2013: 1631- 1642.
[13] KIM Y. Convolutional neural networks for sentence classification[J]. ArXiv Preprint, 2014,arXiv:1408.5882.
[14] PORIA S, CAMBRIA E, GELBUKH A. Deep Convolutional neural network textualfeatures and multiple kernel learning for utterance-level multimodal sentimentAnalysis[C] //Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015:2539-2544.
[15] LIU Pengfei, QIU Xipeng, CHEN Xinchi, et al. Multi-timescale long short-term memory neural network for modelling sentences and documents[C] //Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Beijing:Association for Computational Linguistics, 2015:2326-2335.
[16] LEE J Y, DERNONCOURT F. Sequential short-text classification with recurrent and convolutional neural networks[J]. ArXivpreprint, 2016, arXiv:1603.03827.
[17] ZEILERM D.Adadelta:anadaptive learning rate method[J]. ArXiv Preprint, 2012, arXiv:1212.5701.
[1] . Reader emotion classification with news and comments [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(9): 35-39.
[2] SUN Jian-dong, GU Xiu-sen, LI Yan, XU Wei-ran. Chinese entity relation extraction algorithms based on COAE2016 datasets [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(9): 7-12.
[3] CHEN Jing, LI Shou-shan, ZHOU Guo-dong. User age regression with dual-channel LSTM [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 91-96.
[4] LUO Yi, LI Li, TAN Song-bo, CHENG Xue-qi. Sentiment analysis on Chinese Micro-blog corpus [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 1-7.
[5] XU Guang-zhu1, LIU Ming2, REN Dong1, MA Yi-de3, LIU Xiao-li1. Multi-region image segmentation based on pulse coupled neural network [J]. J4, 2010, 45(7): 86-93.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd