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Cognitive Computation
Erschienen in: Cognitive Computation 4/2017

08.05.2017

A Review of Sentiment Analysis Research in Chinese Language

verfasst von: Haiyun Peng, Erik Cambria, Amir Hussain

Erschienen in: Cognitive Computation | Ausgabe 4/2017

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Abstract

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This review paper aims to study past, present, and future of Chinese sentiment analysis from both monolingual and multilingual perspectives. The constructions of sentiment corpora and lexica are first introduced and summarized. Following, a survey of monolingual sentiment classification in Chinese via three different classification frameworks is conducted. Finally, sentiment classification based on the multilingual approach is introduced. After an overview of the literature, we propose that a more human-like (cognitive) representation of Chinese concepts and their inter-connections could overcome the scarceness of available resources and, hence, improve the state of the art. With the increasing expansion of Chinese language on the Web, sentiment analysis in Chinese is becoming an increasingly important research field. Concept-level sentiment analysis, in particular, is an exciting yet challenging direction for such research field which holds great promise for the future.
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Fußnoten
Literatur
1.
Cambria E, Wang H, White B. Guest editorial: big social data analysis. Knowl-Based Syst. 2014;69: 1–2.CrossRef
2.
Poria S, Cambria E, Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst. 2016;108:42–9.CrossRef
3.
Chaturvedi I, Cambria E, Vilares D. 2016. Lyapunov filtering of objectivity for Spanish sentiment model. In: IJCNN. Vancouver; p. 4474–4481.
4.
Rajagopal D, Cambria E, Olsher D, Kwok K. 2013. A graph-based approach to commonsense concept extraction and semantic similarity detection. In: WWW. Rio De Janeiro; p. 565–570.
5.
Ma Y, Cambria E, Sa G. 2016. Label embedding for zero-shot fine-grained named entity typing. In: COLING. Osaka; p. 171–180.
6.
Poria S, Cambria E, Hazarika D, Vij P. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. In: COLING. p. 1601–1612.
7.
Majumder N, Poria S, Gelbukh A., Cambria E. Deep learning based document modeling for personality detection from text. IEEE Intell. Syst. 2017;32(2):74–9.CrossRef
8.
Mihalcea R, Garimella A. What men say, what women hear: finding gender-specific meaning shades. IEEE Intell Syst. 2016;31(4):62–7.CrossRef
9.
Poria S, Chaturvedi I, Cambria E, Hussain A. 2016. Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: ICDM. Barcelona; p. 439–448.
10.
Quan C, Ren F. 2009. Construction of a blog emotion corpus for chinese emotional expression analysis. In: Proceedings of the 2009 conference on empirical methods in natural language processing: volume 3-volume 3. Association for Computational Linguistics; p. 1446–1454.
11.
Zhao Y. , Qin B., Liu T. Creating a fine-grained corpus for chinese sentiment analysis. IEEE Intell Syst. 2014;30(5):36–43.
12.
Hui-Hsin W, Tsai A C-R, Tsai R T-H, Hsu J Y-J. Building a graded chinese sentiment dictionary based on commonsense knowledge for sentiment analysis of song lyrics. J Inf Sci Eng. 2013;29(4):647–62.
13.
Yan S, Li S. 2013. Constructing chinese sentiment lexicon using bilingual information. In: Chinese lexical semantics. Springer; p. 322–331.
14.
Liu L, Lei M, Wang H. Combining domain-specific sentiment lexicon with hownet for chinese sentiment analysis. J Comput 2013;8(4):878–83.
15.
Hongzhi X, Zhao K, Qiu L, Changjian H. 2010. Expanding chinese sentiment dictionaries from large scale unlabeled corpus. In: PACLIC. p. 301–310.
16.
Ge X, Meng X, Wang H. 2010. Build chinese emotion lexicons using a graph-based algorithm and multiple resources. In: Proceedings of the 23rd international conference on computational linguistics. Association for Computational Linguistics; p. 1209–1217 .
17.
Wang B, Huang Y, Xian W, Li X. A fuzzy computing model for identifying polarity of chinese sentiment words. Comput Intell Neurosci. 2015;2015.
18.
Tan S, Zhang J. An empirical study of sentiment analysis for chinese documents. Expert Syst Appl. 2008; 34(4):2622–9.CrossRef
19.
Zhai Z, Hua X, Kang B, Jia P. Exploiting effective features for chinese sentiment classification. Expert Syst Appl 2011;38(8):9139–46.CrossRef
20.
Zengcai S, Hua X, Zhang D, Yunfeng X. 2014. Chinese sentiment classification using a neural network tool - word2vec. In: 2014 International conference on multisensor fusion and information integration for intelligent systems (MFI). IEEE; p. 1–6.
21.
Xiang L. 2011. Ideogram based chinese sentiment word orientation computation. arXiv preprint arXiv:1110.​4248.
22.
Wei X, Liu Z, Wang T, Liu S. Sentiment recognition of online chinese micro movie reviews using multiple probabilistic reasoning model. J Comput 2013;8(8):1906–11.
23.
Cao Y, Chen Z, Ruifeng X, Chen T, Gui L. A joint model for chinese microblog sentiment analysis. ACL-IJCNLP 2015;2015:61.
24.
Li L, Luo D, Liu M, Zhong J, Ye W, Sun L. 2015. A self-adaptive hidden markov model for emotion classification in chinese microblogs. In: Mathematical problems in engineering .
25.
Lun-Wei K, Huang T-H, Chen H-H. 2009. Using morphological and syntactic structures for chinese opinion analysis. In: Proceedings of the 2009 conference on empirical methods in natural language processing: volume 3-volume 3. Association for Computational Linguistics; p. 1260–1269.
26.
Xiong W, Jin Y, Liu Z. Chinese sentiment analysis using appraiser-degree-negation combinations and pso. J Comput 2014;9(6):1410–7.CrossRef
27.
Zhang H-P, Hong-Kui Y, Xiong D-Y, Liu Q. 2003. Hhmm-based chinese lexical analyzer ictclas. In: Proceedings of the second SIGHAN workshop on Chinese language processing-volume 17. Association for Computational Linguistics; p. 184–187 .
28.
Zhang C, Zeng D, Li J, Wang F-Y, Zuo W. Sentiment analysis of chinese documents: from sentence to document level. J Amer Soc Inf Sci Technol 2009;60(12):2474–87.CrossRef
29.
Zagibalov T, Carroll J. 2008. Automatic seed word selection for unsupervised sentiment classification of chinese text. In: Proceedings of the 22nd international conference on computational linguistics-volume 1. Association for Computational Linguistics; p. 1073–1080.
30.
Zagibalov T, Carroll J. 2008. Unsupervised classification of sentiment and objectivity in chinese text. In: Third international joint conference on natural language processing; p. 304.
31.
Li R, Shi S, Huang H, Chao S, Wang T. 2014. A method of polarity computation of chinese sentiment words based on gaussian distribution. In: Computational linguistics and intelligent text processing. Springer; p. 53–61.
32.
Zhuo S, Xing W, Luo X. 2014. Chinese text sentiment analysis based on fuzzy semantic model. In: 2014 IEEE 13th International conference on cognitive informatics & cognitive computing (ICCI* CC). IEEE; p. 535–540.
33.
Qi S, Xinying X, Guo H, Guo Z, Xian W, Zhang X, Swen Bs, Zhong S. 2008. Hidden sentiment association in chinese web opinion mining. In: Proceedings of the 17th international conference on World Wide Web. ACM; p. 959–968.
34.
Shih-Jung W, Chiang R-D. Using syntactic rules to combine opinion elements in chinese opinion mining systems. J Converg Inf Technol. 2015;10(2):137.
35.
Zhang P, He Z. A weakly supervised approach to chinese sentiment classification using partitioned self-training. J Inf Sci. 2013;39(6):815–31.CrossRef
36.
Bo Y, Liu Y, Li H, Phan T T T, Kausar G, Sing-Bik CN, Wahi W. 2013. Sentiment classification in chinese microblogs: lexicon-based and learning-based approaches. In: International Proceedings of economics development and research (IPEDR), p. 68 .
37.
Wan X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics; p. 553–561 .
38.
He Y, Alani H, Zhou D. 2010. Exploring english lexicon knowledge for chinese sentiment analysis. In: CIPS-SIGHAN joint conference on Chinese language processing.
39.
McArthur T, McArthur F, Vol. 186. The Oxford companion to the English language. Oxford: Oxford University Press; 1992.
40.
Li C, Bo X, Gaowei W, He S, Tian G, Hao H. 2014. Recursive deep learning for sentiment analysis over social data. In: Proceedings of the 2014 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT)-volume 02. IEEE Computer Society; p. 180–185.
41.
Minqing H, Liu B. 2004. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM; p. 168–177.
42.
Li Z, Jing F, Zhu X-Y. 2006. Movie review mining and summarization. In: Proceedings of the 15th ACM international conference on information and knowledge management. ACM; p. 43–50.
43.
Toprak C, Jakob N, Gurevych I. 2010. Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics; p. 575–584.
44.
Lee S Y M, Wang Z. Emotion in code-switching texts: corpus construction and analysis. ACL-IJCNLP 2015; 2015:91.
45.
Liu B. Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 2012;5(1):1–167.CrossRef
46.
Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka E R Jr, Mitchell TM. 2010. Toward an architecture for never-ending language learning. In: AAAI, vol. 5, p. 3.
47.
Lun-Wei K, Liang Y-T, Chen H-H. 2006. Opinion extraction, summarization and tracking in news and blog corpora. In: AAAI spring symposium: computational approaches to analyzing weblogs, vol. 100107.
48.
Dong Z, Dong Q. 2006. HowNet and the computation of meaning. World Scientific.
49.
Baccianella S, Esuli A, Sebastiani F. 2010. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, p. 2200–2204.
50.
Cambria E, Poria S, Bajpai R, Schuller. B. 2016. SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING; p. 2666–2677.
51.
Dong Z, Dong Q, Hao C. 2010. Hownet and its computation of meaning. In: Proceedings of the 23rd international conference on computational linguistics: demonstrations. Association for Computational Linguistics; p. 53–56.
52.
Gao D, Wei F, Li W, Liu X, Zhou M. 2015. Cross-lingual sentiment lexicon learning with bilingual word graph label propagation. Comput Linguist.
53.
Peng H, Cambria E. 2017. CSenticNet: a concept-level resource for sentiment analysis in Chinese languagel. In: CICLing.
54.
Tan L, Bond F. Building and annotating the linguistically diverse ntu-mc (ntu-multilingual corpus). Int J Asian Lang Proc 2012;22(4):161–74.
55.
Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst 2016;31(2):102–7.CrossRef
56.
Li Z, Sun M. Punctuation as implicit annotations for chinese word segmentation. Comput Linguist 2009; 35(4):505–12.CrossRef
57.
Bird S, Klein E, Edward L. 2009. Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media Inc.
58.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30.
59.
Chen X, Lei X, Liu Z, Sun M, Luan H. 2015. Joint learning of character and word embeddings. In: IJCAI, p. 1236–1242.
60.
Sun Y, Lin L, Yang N, Ji Z, Wang X. 2014. Radical-enhanced chinese character embedding. In: LNCS, vol. 8835, p. 279–286.
61.
Shi X, Zhai J, Yang X, Xie Z, Liu C. 2015. Radical embedding: delving deeper to chinese radicals. In: ACL, p. 594–598.
62.
Yin R, Wang Q, Li R, Li P, Wang B. 2016. Distributed representations of words and phrases and their compositionality. In: EMNLP, p. 981–986.
63.
Peng H, Cambria E, Zou X. 2017. Radical-based hierarchical embeddings for chinese sentiment analysis at sentence level. In: The 30th International FLAIRS conference. Marco Island.
64.
Eckman P. 1972. Universal and cultural differences in facial expression of emotion. In: Nebraska symposium on motivation, vol. 19. University of Nebraska Press Lincoln; p. 207–284 .
65.
Wei G, An H, Dong T, Li H. 2014. A novel micro-blog sentiment analysis approach by longest common sequence and k-medoids. PACIS, p. 38.
66.
Chen Z, Huang Y, Tian J, Liu X, Kun F, Huang T. 2015. Joint model for subsentence-level sentiment analysis with markov logic. J Assoc Inf Sci Technol.
67.
Liu S M, Chen J-H. A multi-label classification based approach for sentiment classification. Expert Syst Appl 2015;42(3):1083–93.CrossRef
68.
Quan C, Wei X, Ren F. 2013. Combine sentiment lexicon and dependency parsing for sentiment classification. In: 2013 IEEE/SICE International symposium on system integration (SII). IEEE; p. 100–104.
69.
Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015.CrossRef
70.
Cambria E, White B. Jumping nlp curves: a review of natural language processing research. IEEE Comput Intell Mag 2014;9(2):48–57.CrossRef
71.
Li Q, Zhi Q, Li M. An combined sentiment classification system for sighan-8. ACL-IJCNLP 2015;2015: 74.
72.
Wen S, Wan X. 2014. Emotion classification in microblog texts using class sequential rules. In: Twenty-Eighth AAAI conference on artificial intelligence.
73.
Wei B, Pal C. 2010. Cross lingual adaptation: an experiment on sentiment classifications. In: Proceedings of the ACL 2010 conference short papers. Association for Computational Linguistics; p. 258–262.
74.
Poria S, Chaturvedi I, Cambria E, Bisio F. 2016. Sentic LDA: improving on LDA with semantic similarity for aspect-based sentiment analysis. In: IJCNN, p. 4465–4473.
75.
Bin L, Tan C, Cardie C, Tsou B K. 2011. Joint bilingual sentiment classification with unlabeled parallel corpora. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-volume 1. Association for Computational Linguistics; p. 320–330.
76.
Zhou H, Chen L, Shi F, Huang D. 2015. Learning bilingual sentiment word embeddings for cross-language sentiment classification. In: ACL.
77.
Chen Q, Li W, Lei Y, Liu X, He Y. 2015. Learning to adapt credible knowledge in cross-lingual sentiment analysis. In: Proc. 53rd Annu. meeting assoc. comput. linguist.
78.
Xianghua F, Yingying X. 2015. Recursive autoencoder with hownet lexicon for sentence-level sentiment analysis. In: Proceedings of the ASE bigdata & socialinformatics 2015. ACM; p. 20 .
79.
Huang Z, Zhao Z, Liu Q, Wang Z. 2015. An unsupervised method for short-text sentiment analysis based on analysis of massive data. In: International conference of young computer scientists, engineers and educators. Springer; p. 169–176.
80.
Zhang D, Hua X, Zengcai S, Yunfeng X. 2015. Chinese comments sentiment classification based on word2vec and svm perf, Vol. 42.
Metadaten
Titel
A Review of Sentiment Analysis Research in Chinese Language
verfasst von
Haiyun Peng
Erik Cambria
Amir Hussain
Publikationsdatum
08.05.2017
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 4/2017
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-017-9470-8

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