Volume 19, Issue 2 (9-2022)                   JSDP 2022, 19(2): 107-132 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rajabi Z, valavi M, Hourali M. Sentiment analysis methods in Persian text: A survey. JSDP 2022; 19 (2) :107-132
URL: http://jsdp.rcisp.ac.ir/article-1-1099-en.html
Iran Telecommunication Research Center (ITRC)
Abstract:   (425 Views)
With the explosive growth of social media such as Twitter and Instagram, reviews on e-commerce websites, and comments on news websites, individuals and organizations are increasingly using analyzing opinions in these media for their decision-making and designing strategies. Sentiment analysis is one of the techniques used to analyze users' opinions in recent years. The Persian language has specific features and thereby requires unique methods and models to be adopted for sentiment analysis, which are different from those in English and other languages. This paper identifies the characteristics and limitations of the Persian language. Sentiment analysis in each language has specified prerequisites; hence, the direct use of methods, tools, and resources developed for the English language in Persian has its limitations.
The present study aims to investigate and compare previous sentiment analysis studies on Persian texts and describe views presented in articles published in the last decade. First, the sentiment analysis levels, approaches, and tasks are described. Then, a detailed survey of the applied sentiment analysis methods used for Persian texts is presented, and previous works in this field are discussed. The advantages and disadvantages of each proposed method are demonstrated. Moreover, the publicly available sentiment analysis resources of Persian texts are studied, and the characteristics and differences of each are highlighted.
As a result, according to the recent development of the sentiment analysis field, some issues and challenges not being addressed in Persian texts are listed, and some guidelines are provided for future research on Persian texts. Future requirements of Persian text for improving the sentiment analysis system are detailed.
Article number: 8
Full-Text [PDF 1363 kb]   (239 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2019/12/15 | Accepted: 2021/05/23 | Published: 2022/09/30 | ePublished: 2022/09/30

References
1. [1] E. Cambria, S. Poria, A. Hussain, and B. Liu, "Computational Intelligence for Affective Computing and Sentiment Analysis [Guest Editorial]", IEEE Computational Intelligence Magazine, vol. 14(2), pp. 16-17, 2019. [DOI:10.1109/MCI.2019.2901082]
2. [2] B. Liu, "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies, vol. 5(1), pp. 1-167, 2012. [DOI:10.2200/S00416ED1V01Y201204HLT016]
3. [3] B. Liu, "Sentiment Analysis and Subjectivity", Handbook of natural language processing, vol. 2, pp. 627-666, 2010.
4. [4] A. Keramatfar, and H. Amirkhani, "Bibliometrics of sentiment analysis literature", Journal of Information Science, vol. 45(1), pp. 3-15, 2019. [DOI:10.1177/0165551518761013]
5. [5] R. Piryani, D. Madhavi, and V.K. Singh, "Analytical mapping of opinion mining and sentiment analysis research during 2000-2015", Information Processing & Management, vol.53(1), pp. 122-150, 2017. [DOI:10.1016/j.ipm.2016.07.001]
6. [6] W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol.5(4), pp. 1093-1113, 2014. [DOI:10.1016/j.asej.2014.04.011]
7. [7] K. Ravi, and V. Ravi, "A survey on opinion mining and sentiment analysis: Tasks, approaches and applications", Knowledge-Based Systems, vol.89, pp. 14-46, 2015. [DOI:10.1016/j.knosys.2015.06.015]
8. [8] A. Montoyo, P. MartíNez-Barco, and A. Balahur, "Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments", Decision Support Systems, vol. 53(4), pp. 675-679, 2012. [DOI:10.1016/j.dss.2012.05.022]
9. [9] A.N. Jebaseeli, and E. Kirubakaran, "A survey on sentiment analysis of (product) reviews", International Journal of Computer Applications, vol. 47(11), 2012. [DOI:10.5120/7234-0242]
10. [10] D. Hussein, "A survey on sentiment analysis challenges", Journal of King Saud University-Engineering Sciences, vol. 30(4), pp. 330-338, 2018. [DOI:10.1016/j.jksues.2016.04.002]
11. [11] N. Boudad, R. Faizi, R.O.H. Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature", Ain Shams Engineering Journal, 2017. [DOI:10.1016/j.asej.2017.04.007]
12. [12] S.L. Lo, E. Cambria, R. Chiong, and D. Cornforth, "Multilingual sentiment analysis: from formal to informal and scarce resource languages", Artificial Intelligence Review,. Vol.48(4), pp. 499-527, 2017. [DOI:10.1007/s10462-016-9508-4]
13. [13] K. Dashtipour, S. Poria, A. Hussain, E. Cambria, A.Y. Hawalah, A. Gelbukh, and Q. Zhou, "Multilingual sentiment analysis: state of the art and independent comparison of techniques", Cognitive computation, vol.8(4), pp. 757-771, 2016. [DOI:10.1007/s12559-016-9415-7] [PMID] [PMCID]
14. [14] E.F. Can, A. Ezen-Can, and F. Can, "Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data", arXiv preprint arXiv:1806.04511, 2018.
15. [15] A. Balahur, J.M. Hermida, and A. Montoyo, "Detecting implicit expressions of sentiment in text based on commonsense knowledge", in Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, 2011, Association for Computational Linguistics.
16. [16] M.E. Basiri, A.R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in persian" Open Transactions on Information Processing, Vol.1(3), pp. 1-14, 2014. [DOI:10.15764/OTIP.2014.03001]
17. [17] E. Asgarian, M. Kahani, and S. Sharifi, "The impact of sentiment features on the sentiment polarity classification in Persian reviews", Cognitive Computation, vol. 10(1), pp. 117-135, 2018. [DOI:10.1007/s12559-017-9513-1]
18. [18] R. Dehkharghani, "SentiFars: A Persian Polarity Lexicon for Sentiment Analysis", ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19(2), pp. 21, 2019. [DOI:10.1145/3345627]
19. [19] E. Cambria, "An introduction to concept-level sentiment analysis," in Mexican International Conference on Artificial Intelligence, 2013. Springer.
20. [20] N. Cristianini, and J. Shawe-Taylor, "An introduction to support vector machines and other kernel-based learning methods", 2000: Cambridge university press. [DOI:10.1017/CBO9780511801389] [PMCID]
21. [21] T. Joachims, "Making large-scale svm learning practical", I n A dvan ces in Ker n e l Meth od s. 1998, MIT Press.
22. [22] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques", in Proceedings of the ACL-02 conference on Empirical methods in natural language processing,Vol. 10, 2002, [DOI:10.3115/1118693.1118704]
23. [23] P.D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews", in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, Association for Computational Linguistics. [DOI:10.3115/1073083.1073153]
24. [24] S. Dasgupta, and V. Ng, "Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification", in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009. [DOI:10.3115/1690219.1690244]
25. [25] S. Poria, E. Cambria, G. Winterstein, and G.-B. Huang, "Sentic patterns: Dependency-based rules for concept-level sentiment analysis", Knowledge-Based Systems, Vol. 69, pp. 45-63, 2014. [DOI:10.1016/j.knosys.2014.05.005]
26. [26] E. Cambria, and A. Hussain, "Sentic computing: a common-sense-based framework for concept-level sentiment analysis", Springer, Vol. 1. 2015.
27. [27] E. Cambria, S. Poria, F. Bisio, R. Bajpai, and I. Chaturvedi, "The CLSA model: a novel framework for concept-level sentiment analysis", in International Conference on Intelligent Text Processing and Computational Linguistics, Springer, 2015. [DOI:10.1007/978-3-319-18117-2_1]
28. [28] Z. Rajabi, M.R. Valavi, and M. Hourali, "A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach", Cognitive Computation, pp. 1-14, 2020. [DOI:10.1007/s12559-020-09729-1]
29. [29] K. Dashtipour, M. Gogate, J. Li, F. Jiang, B. Kong, and A. Hussain, "A hybrid Persian sentiment analysis framework: Integrating dependency grammar-based rules and deep neural networks", Neurocomputing, 2020. 380: pp. 1-10. [DOI:10.1016/j.neucom.2019.10.009]
30. [30] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, Feature extraction in opinion mining for Persian text, in 2nd National Conference on Computer Science, 2013.
31. [31] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015. [DOI:10.5829/idosi.JAIDM.2015.03.02.06]
32. [32] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015. [DOI:10.5829/idosi.JAIDM.2015.03.02.06]
33. [33] F. Mirojagh, M. Shahraki, "Fake opinion detection using a supervised approach", International Conference on Electrical Engineering and Computer Science, 2014.
34. [34] S. Zirpe, and B. Joglekar, "Polarity shift detection approaches in sentiment analysis: A survey", in 2017 International Conference on Inventive Systems and Control (ICISC), IEEE, 2017. [DOI:10.1109/ICISC.2017.8068737]
35. [35] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), IEEE, 2012. [DOI:10.1109/AISP.2012.6313747]
36. [36] A. Bagheri, and M. Saraee, "Persian sentiment analyzer: A framework based on a novel feature selection method", International Journal of Artificial Intelligence, vol. 12(2), pp. 115-129, 2014.
37. [37] A. Bagheri, M. Saraee, and F. de Jong. "Sentiment classification in Persian: Introducing a mutual information-based method for feature selection", in 2013 21st Iranian Conference on Electrical Engineering (ICEE), IEEE, 2013. [DOI:10.1109/IranianCEE.2013.6599671] [PMID]
38. [38] E. Vaziripour, C. Giraud-Carrier, and D. Zappala, "Analyzing the political sentiment of Tweets in Farsi", in Tenth International AAAI Conference on Web and Social Media, 2016.
39. [39] F. Amiri, S. Scerri, and M. Khodashahi. "Lexicon-based sentiment analysis for Persian Text", in Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015.
40. [40] S. Alimardani, and A. Aghaie, Opinion mining in Persian language using supervised algorithms, Journal of Information Systems and Telecommunication (JIST), 2015.
41. [41] S. Alimardani, and A. AGHAIE, "Opinion mining in Persian language using supervised algorithms and sentiment lexicon", Journal of Information Technology Management, vol.7, pp. 345-362, 2015.
42. [42] S. Sadidpour, , H. Shirazi, N.M. Sharef, B. Minaei-Bidgoli, and M.E. Sanjaghi, "Context-Sensitive Opinion Mining using Polarity Patterns", International Journal of Advanced Computer Science and Applications (IJACSA), pp.7, 2016. [DOI:10.14569/IJACSA.2016.070920]
43. [43] M. Saraee, and A. Bagheri, "Feature selection methods in Persian sentiment analysis", in International Conference on Application of Natural Language to Information Systems. Springer, 2013. [DOI:10.1007/978-3-642-38824-8_29]
44. [44] Li, Y, H. Guo, Q. Zhang, M. Gu, and J. Yang, "Imbalanced text sentiment classification using universal and domain-specific knowledge," Knowledge-Based Systems, vol.160, pp. 1-15, 2018. [DOI:10.1016/j.knosys.2018.06.019]
45. [45] S. Noferesti, and M. Shamsfard, "Using Linked Data for polarity classification of patients' experiences", Journal of biomedical informatics,vol. 57, pp. 6-19, 2015. [DOI:10.1016/j.jbi.2015.06.017] [PMID]
46. [46] M.Najafzadeh, S. Rahati Quchan, and R. Ghaemi, "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing,vol. 15(2), pp. 89-102, 2018. [DOI:10.29252/jsdp.15.2.89]
47. [47] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages", Neural Networks, vol. 58, pp. 50-59, 2014. [DOI:10.1016/j.neunet.2014.05.018] [PMID]
48. [48] E.Asgarian, , M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet", Signal and Data Processing,vol, vol.15(1), pp. 71-86, 2018. [DOI:10.29252/jsdp.15.1.71]
49. [49] B. Sabeti, , P. Hosseini, G. Ghassem-Sani, and S.A. Mirroshandel, LexiPers: An ontology based sentiment lexicon for Persian. in GCAI. 2016.
50. [50] S. Deng, A.P. Sinha, and H. Zhao, "Adapting sentiment lexicons to domain-specific social media texts", Decision Support Systems, vol. 94, pp. 65-76, 2017. [DOI:10.1016/j.dss.2016.11.001]
51. [51] P. Hosseini, H. Maleki, A.A. Ramaki, , M. Anvari, and S.A. Mirroshandel, "A sentiment analysis corpus for Persian(SentiPers)," in 3rd National Conference on Computational Linguistics, 2013.
52. [52] P. Hosseini, A.A. Ramaki, H. Maleki, M. Anvari, and S.A. Mirroshandel, SentiPers: a sentiment analysis corpus for Persian. arXiv preprint arXiv:1801.07737, 2018.
53. [53] M. Moradi, P. Khosravizade, and V. Bahram, "Constructing tagged corpora with a web approach as a corpus", in 2th symposium on computational Linguistics, 2012.
54. [54] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A.Y. Hawalah, and E. Cambria. "PerSent: a freely available Persian sentiment lexicon", in International Conference on Brain Inspired Cognitive Systems, 2016, Springer. [DOI:10.1007/978-3-319-49685-6_28]
55. [55] A. Esuli, and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC. 2006. Citeseer.
56. [56] P.J. Stone, D.C. Dunphy, and M.S. Smith, The general inquirer: A computer approach to content analysis, 1966.
57. [57] T.Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity in phrase-level sentiment analysis", in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005. [DOI:10.3115/1220575.1220619] [PMCID]
58. [58] C.H.E. Gilbert, Vader: A parsimonious rule-based model for sentiment analysis of social media text. in Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20/04/16) http://comp. social. gatech. edu/papers/icwsm14. vader. hutto. pdf. 2014.
59. [59] S. Huang, Z. Niu, and C. Shi, "Automatic construction of domain-specific sentiment lexicon based on constrained label propagation," Knowledge-Based Systems, vol. 56, pp. 191-200, 2014. [DOI:10.1016/j.knosys.2013.11.009]
60. [60] S.Tan, and Q. Wu, "A random walk algorithm for automatic construction of domain-oriented sentiment lexicon," Expert Systems with Applications, vol. 38(10), pp. 12094-12100, 2011. [DOI:10.1016/j.eswa.2011.02.105]
61. [61] K. Dashtipour, A. Raza, A. Gelbukh, R. Zhang, E. Cambria, and A. Hussain, "PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words", in International Conference on Brain Inspired Cognitive Systems, Springer, 2019. [DOI:10.1007/978-3-030-39431-8_48]
62. [62] A. Balahur, and G. Jacquet, Sentiment analysis meets social media-Challenges and solutions of the field in view of the current information sharing context. Information Processing & Management, 2015. 51(4): p. 428-432. [DOI:10.1016/j.ipm.2015.05.005]
63. [63] D. Osimo, and F. Mureddu, "Research challenge on opinion mining and sentiment analysis", Universite de Paris-Sud, Laboratoire LIMSI-CNRS, Bâtiment, 2012. 508.
64. [64] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis", IEEE Intelligent Systems, vol. 28(2), pp. 15-21, 2013. [DOI:10.1109/MIS.2013.30]
65. [65] Z. Rajabi, M. Valavi, and M. Hourali, "A model for enriching sentiment lexicon based on semantic knowledge base", 10st Iranian C4I Conference, 2017.
66. [66] Z. Rajabi, M. Valavi, and M. Hourali, "A context-based model for disambiguating the sentiment concepts using the common-sense knowledge", C4I Journal, vol.2(2), pp. 32-47, 2018.
67. [67] J. Liao, S. Wang, and D. Li, "Identification of fact-implied implicit sentiment based on multi-level semantic fused representation" Knowledge-Based Systems, vol.165, pp. 197-207, 2019. [DOI:10.1016/j.knosys.2018.11.023]
68. [68] C. Hung, "Word of mouth quality classification based on contextual sentiment lexicons", Information Processing & Management, vol. 53(4), pp. 751-763, 2017. [DOI:10.1016/j.ipm.2017.02.007]
69. [69] H. Saif, Y. He, M. Fernandez, and H. Alani, "Contextual semantics for sentiment analysis of Twitter", Information Processing & Management, vol. 52(1), pp. 5-19, 2016. [DOI:10.1016/j.ipm.2015.01.005]
70. [70] S. Noferesti, and M. Shamsfard, "Resource construction and evaluation for indirect opinion mining of drug reviews", PloS one, vol. 10(5), pp. e0124993, 2015. [DOI:10.1371/journal.pone.0124993] [PMID] [PMCID]
71. [71] L. Zhang, S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews", Data Mining and Knowledge Discovery, 2018. Vol.8(4), p p,1253. [DOI:10.1002/widm.1253]
72. [72] T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent trends in deep learning based natural language processing", ieee Computational intelligenCe magazine, vol.13(3), pp. 55-75, 2018. [DOI:10.1109/MCI.2018.2840738]
73. [73] K. Dashtipour, M. Gogate, A. Adeel, C. Ieracitano, H. Larijani, and A. Hussain, "Exploiting deep learning for persian sentiment analysis", in International Conference on Brain Inspired Cognitive Systems, Springer, 2018. [DOI:10.1007/978-3-030-00563-4_58]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing