Skip to main content
SpringerLink
Log in

A Survey on Sentiment Analysis in Persian: a Comprehensive System Perspective Covering Challenges and Advances in Resources and Methods

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Social media has been remarkably grown during the past few years. Nowadays, posting messages on social media websites has become one of the most popular Internet activities. The vast amount of user-generated content has made social media the most extensive data source of public opinion. Sentiment analysis is one of the techniques used to analyze user-generated data. 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 language. Sentiment analysis in each language has specified prerequisites; hence, the direct use of methods, tools, and resources developed for English language in Persian has its limitations. The main target of this paper is to provide a comprehensive literature survey for state-of-the-art advances in Persian sentiment analysis. In this regard, the present study aims to investigate and compare the previous sentiment analysis studies on Persian texts and describe contributions presented in articles published in the last decade. First, the levels, approaches, and tasks for sentiment analysis are described. Then, a detailed survey of the sentiment analysis methods used for Persian texts is presented, and previous relevant works on Persian Language are discussed. Moreover, we present in this survey the authentic and published standard sentiment analysis resources and advances that have been done for Persian sentiment analysis. Finally, according to the state-of-the-art development of English sentiment analysis, some issues and challenges not being addressed in Persian texts are listed, and some guidelines and trends are provided for future research on Persian texts. The paper provides information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete sentiment analysis system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cambria E, Poria S, Hussain A, Liu B. Computational intelligence for affective computing and sentiment analysis [Guest Editorial]. IEEE Comput Intell Mag. 2019;14(2):16–7.

    Article  Google Scholar 

  2. Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. 2012;5(1):1–167.

    Article  Google Scholar 

  3. Liu B. Sentiment analysis and subjectivity. Handbook of natural language processing. 2010;2:627–66.

    Google Scholar 

  4. Piryani R, Madhavi D, Singh VK. Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf Process Manag. 2017;53(1):122–50.

    Article  Google Scholar 

  5. Keramatfar A, Amirkhani H. Bibliometrics of sentiment analysis literature. J Inf Sci. 2019;45(1):3–15.

    Article  Google Scholar 

  6. Jebaseeli AN, Kirubakaran E. A survey on sentiment analysis of (product) reviews. Int J Comput Appl. 2012;47(11).

  7. Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng J. 2014;5(4):1093–113.

    Article  Google Scholar 

  8. Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl-Based Syst. 2015;89:14–46.

    Article  Google Scholar 

  9. Hussein DMEDM. A survey on sentiment analysis challenges. J King Saud Univ Eng Sci. 2018;30(4):330–338.

  10. Montoyo A, MartíNez-Barco P, Balahur A. Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments. Decis Support Syst. 2012;53(4):675–9.

    Article  Google Scholar 

  11. Boudad N, Faizi R, Thami ROH, Chiheb R. Sentiment analysis in Arabic: A review of the literature. Ain Shams Eng J. 2017;2479–2490.

  12. Badaro G, Baly R, Hajj H, El-Hajj W, Shaban KB, Habash N, Al-Sallab A, Hamdi A. A survey of opinion mining in Arabic: a comprehensive system perspective covering challenges and advances in tools, resources, models, applications, and visualizations. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 2019;18(3):1–52.

    Article  Google Scholar 

  13. Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AY, Gelbukh A, Zhou Q. Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn Comput. 2016;8(4):757–71.

    Article  Google Scholar 

  14. Lo SL, Cambria E, Chiong R, Cornforth D. Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif Intell Rev. 2017;48(4):499–527.

    Article  Google Scholar 

  15. Can EF, Ezen-Can A, Can F. Multilingual sentiment analysis: an RNN-based framework for limited data. arXiv preprint arXiv:1806.04511. 2018.

  16. Speer R, Chin J, Havasi C. Conceptnet 5.5: An open multilingual graph of general knowledge. In Thirty-First AAAI Conference on Artificial Intelligence. 2017.

  17. Balahur A, Hermida JM, Montoyo A. 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. Association for Computational Linguistics. 2011.

  18. Basiri ME, Naghsh-Nilchi AR, Ghassem-Aghaee N. A framework for sentiment analysis in persian. Open Transactions on Information Processing. 2014;1(3):1–14.

    Article  Google Scholar 

  19. Asgarian E, Kahani M, Sharifi S. The impact of sentiment features on the sentiment polarity classification in Persian reviews. Cogn Comput. 2018;10(1):117–35.

    Article  Google Scholar 

  20. Dehkharghani R. SentiFars: a Persian polarity lexicon for sentiment analysis. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 2019;19(2):21.

    Google Scholar 

  21. Ghasemi R, Ashrafi Asli SA, Momtazi S. Deep Persian sentiment analysis: cross-lingual training for low-resource languages. J Inf Sci. 2020;0165551520962781.

  22. Dastgheib MB, Koleini S, Rasti F. The application of deep learning in Persian documents sentiment analysis. International Journal of Information Science and Management (IJISM). 2020;18(1):1–15.

    Google Scholar 

  23. Cambria, E. An introduction to concept-level sentiment analysis. In Mexican International Conference on Artificial Intelligence. Springer. 2013.

  24. Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics. 2002.

  25. Fu X, Yang J, Li J, Fang M, Wang H. Lexicon-enhanced LSTM with attention for general sentiment analysis. IEEE Access. 2018;6:71884–91.

    Article  Google Scholar 

  26. Turney PD. 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. Association for Computational Linguistics. 2002.

  27. Poria S, Cambria E, Winterstein G, Huang G-B. Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl-Based Syst. 2014;69:45–63.

    Article  Google Scholar 

  28. Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Springer. 2015;1.

  29. Cambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intell Syst. 2013;28(2):15–21.

    Article  Google Scholar 

  30. Cambria E, Poria S, Bisio F, Bajpai R, Chaturvedi I. The CLSA model: a novel framework for concept-level sentiment analysis. In International Conference on Intelligent Text Processing and Computational Linguistics. Springer. 2015.

  31. Rajabi Z, Hourali M, Valavi M. A context-based model for disambiguating the sentiment concepts using the common-sense knowledge. C4I Journal. 2018;2(2):32–47.

  32. Rajabi Z, Valavi MR, Hourali M. A context-based disambiguation model for sentiment concepts using a bag-of-concepts approach. Cogn Comput. 2020;12(6):1299–312.

    Article  Google Scholar 

  33. Shams M, Shakery A, Faili H. 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.

  34. Bagheri A, Saraee M, de Jong F. Sentiment classification in Persian: introducing a mutual information-based method for feature selection. In 2013 21st Iranian Conference on Electrical Engineering (ICEE). IEEE. 2013.

  35. Bagheri A, Saraee M. Persian sentiment analyzer: a framework based on a novel feature selection method. International Journal of Artificial Intelligence. 2014;12(2):115–29.

    Google Scholar 

  36. Vaziripour E, Giraud-Carrier C, Zappala D. Analyzing the political sentiment of Tweets in Farsi. In Tenth International AAAI Conference on Web and Social Media. 2016.

  37. Amiri F, Scerri S, Khodashahi M. Lexicon-based sentiment analysis for Persian text. In Proceedings of the International Conference Recent Advances in Natural Language Processing. 2015.

  38. Alimardani S, Aghaie A. Opinion mining in Persian language using supervised algorithms. J Inf Syst Telecommun (JIST). 2015;135–141.

  39. Alimardani S, Aghaie A. Opinion mining in Persian language using supervised algorithms and sentiment lexicon. Int J Inf Technol Manag. 2015;7:345–62.

    Google Scholar 

  40. Sadidpour S, Shirazi H, Sharef NM, Minaei-Bidgoli B, Sanjaghi ME. Context-sensitive opinion mining using polarity patterns. Int J Adv Comput Sci Appl (IJACSA). 2016;7:145–50.

    Google Scholar 

  41. Dashtipour K, Gogate M, Li J, Jiang F, Kong B, Hussain A. A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing. 2020;380:1–10.

    Article  Google Scholar 

  42. Dashtipour K, Gogate M, Adeel A, Ieracitano C, Larijani H, Hussain A. Exploiting deep learning for persian sentiment analysis. In International Conference on Brain Inspired Cognitive Systems. Springer. 2018.

  43. Bagheri A. Integrating word status for joint detection of sentiment and aspect in reviews. J Inf Sci. 2019;45(6):736–55.

    Article  Google Scholar 

  44. Roshanfekr B, Khadivi S, Rahmati M. Sentiment analysis using deep learning on Persian texts. In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE. 2017.

  45. Zobeidi S, Naderan M, Alavi SE. Opinion mining in Persian language using a hybrid feature extraction approach based on convolutional neural network. Multimed Tools Appl. 2019;78(22):32357–78.

    Article  Google Scholar 

  46. Ashrafi Asli SA, Sabeti B, Majdabadi Z, Golazizian P, Momenzadeh O. Optimizing annotation effort using active learning strategies: a sentiment analysis case study in persian. In Proceedings of The 12th Language Resources and Evaluation Conference. 2020.

  47. Saraee M, Bagheri A. Feature selection methods in Persian sentiment analysis. in International Conference on Application of Natural Language to Information Systems. Springer. 2013.

  48. Li Y, Guo H, Zhang Q, Gu M, Yang J. Imbalanced text sentiment classification using universal and domain-specific knowledge. Knowl-Based Syst. 2018;160:1–15.

    Article  Google Scholar 

  49. Noferesti S, Shamsfard M. Using linked data for polarity classification of patients’ experiences. J Biomed Inform. 2015;57:6–19.

    Article  Google Scholar 

  50. Noferesti S, Shamsfard M. Resource construction and evaluation for indirect opinion mining of drug reviews. PLoS One. 2015;10(5):e0124993.

    Article  Google Scholar 

  51. Dehdarbehbahani I, Shakery A, Faili H. Semi-supervised word polarity identification in resource-lean languages. Neural Netw. 2014;58:50–9.

    Article  Google Scholar 

  52. Najafzadeh M, Rahati Quchan S, Ghaemi R. A semi-supervised framework based on self-constructed adaptive lexicon for Persian sentiment analysis. Signal and Data Processing. 2018;15(2):89–102.

  53. Golpar-Rabooki E, Zarghamifar S, Rezaeenour J. Feature extraction in opinion mining for Persian text. In 2nd National Conference on Computer Science. 2013.

  54. Golpar-Rabooki E, Zarghamifar S, Rezaeenour J. Feature extraction and vocabulary expansion in opinion mining for Persian text. In 2nd National Conference on Applied Research in Computer Science and Information Technology. 2015.

  55. Golpar-Rabooki E, Zarghamifar S, Rezaeenour J. Feature extraction in opinion mining through Persian reviews. Journal of AI and Data Mining. 2015;3(2):169–79.

    Google Scholar 

  56. Golazizian P, Sabeti B, Asli SAA, Majdabadi Z, Momenzadeh O. Irony detection in Persian language: a transfer learning approach using emoji prediction. In Proceedings of The 12th Language Resources and Evaluation Conference. 2020.

  57. Zirpe S, Joglekar B. Polarity shift detection approaches in sentiment analysis: a survey. in 2017 International Conference on Inventive Systems and Control (ICISC). IEEE. 2017.

  58. Asgarian E, Kahani M, Sharifi S. HesNegar: Persian sentiment WordNet. Signal and Data Processing. 2018;15(1):71–86.

    Article  Google Scholar 

  59. Sabeti B, Hosseini P, Ghassem-Sani G, Mirroshandel SA. LexiPers: an ontology based sentiment lexicon for Persian. In GCAI. 2016.

  60. Dashtipour K, Hussain A, Zhou Q, Gelbukh A, Hawalah AY, Cambria E. PerSent: a freely available Persian sentiment lexicon. In International Conference on Brain Inspired Cognitive Systems. Springer. 2016.

  61. Hosseini P, Ramaki AA, Maleki H, Anvari M, Mirroshandel SA. SentiPers: a sentiment analysis corpus for Persian. arXiv preprint arXiv:1801.07737. 2018.

  62. Akhoundzade R, Devin KH. Persian sentiment lexicon expansion using unsupervised learning methods. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE. 2019.

  63. Moradi M, Parvane K, Bahram V. Constructing tagged corpora with a web approach as a corpus. In 2th symposium on computational Linguistics. 2012.

  64. Deng S, Sinha AP, Zhao H. Adapting sentiment lexicons to domain-specific social media texts. Decis Support Syst. 2017;94:65–76.

    Article  Google Scholar 

  65. Esuli A, Sebastiani F. Sentiwordnet: a publicly available lexical resource for opinion mining. in Proceedings of LREC. Citeseer. 2006.

  66. Wilson T, Wiebe J, Hoffmann P. 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.

  67. Stone PJ, Dunphy DC, Smith MS. The general inquirer: a computer approach to content analysis. 1966.

  68. Gilbert CHE. 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.

  69. Tan S, Wu Q. A random walk algorithm for automatic construction of domain-oriented sentiment lexicon. Expert Syst Appl. 2011;38(10):12094–100.

    Article  Google Scholar 

  70. Huang S, Niu Z, Shi C. Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl-Based Syst. 2014;56:191–200.

    Article  Google Scholar 

  71. Dashtipour K, Raza A, Gelbukh A, Zhang R, Cambria E, Hussain A. PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words. In International Conference on Brain Inspired Cognitive Systems. Springer. 2019.

  72. Liao J, Wang S, Li D. Identification of fact-implied implicit sentiment based on multi-level semantic fused representation. Knowl-Based Syst. 2019;165:197–207.

    Article  Google Scholar 

  73. Hung C. Word of mouth quality classification based on contextual sentiment lexicons. Inf Process Manag. 2017;53(4):751–63.

    Article  Google Scholar 

  74. Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Inf Process Manag. 2016;52(1):5–19.

    Article  Google Scholar 

  75. Zhang L, Wang S, Liu B. Deep learning for sentiment analysis: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2018;8(4):e1253.

    Google Scholar 

  76. Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag. 2018;13(3):55–75.

  77. Dashtipour K, Gogate M, Cambria E, Hussain A. A novel context-aware multimodal framework for Persian sentiment analysis. arXiv preprint arXiv:2103.02636. 2021.

Download references

Author information

Authors and Affiliations

  1. Department of Electronic and Computer, Malek-Ashtar University of Technology, Tehran, Iran

    Zeinab Rajabi & MohammadReza Valavi

  2. Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

    Zeinab Rajabi

Authors
  1. Zeinab Rajabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. MohammadReza Valavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeinab Rajabi.

Ethics declarations

Informed Consent

Informed consent was not required as no human or animals were involved.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajabi, Z., Valavi, M. A Survey on Sentiment Analysis in Persian: a Comprehensive System Perspective Covering Challenges and Advances in Resources and Methods. Cogn Comput 13, 882–902 (2021). https://doi.org/10.1007/s12559-021-09886-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09886-x

Keywords

Search

Navigation