Skip to main content
SpringerLink
Log in

Adaptation of Sentiment Analysis Techniques to Persian Language

  • Conference paper
  • First Online:
Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment analysis research has been growing rapidly; however, most of the research papers are focused on English. In this paper, we review English-based sentiment analysis approaches and discuss what adaption these approaches require to become applicable to the Persian language. The results show that approaches initially suggested for English language are competitive with those developed specifically for Persian sentiment analysis.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adeel, A., et al.: A survey on the role of wireless sensor networks and IoT in disaster management. In: Durrani, T., Wang, W., Forbes, S. (eds.) Geological Disaster Monitoring Based on Sensor Networks, pp. 57–66. Springer Natural Hazards. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0992-2_5

    Google Scholar 

  • Balahur, A., et al.: Sentiment analysis in the news. ArXiv Prepr. ArXiv:13096202, pp. 2216–2220 (2013)

    Google Scholar 

  • Basiri, M.E., Naghsh-Nilchi, A.R., Ghassem-Aghaee, N.: A framework for sentiment analysis in Persian. Open Trans. Inf. Process. 1, 14–18 (2014)

    Google Scholar 

  • Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)

    Article  Google Scholar 

  • Cambria, E., Havasi, C., Hussain, A.: SenticNet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: FLAIRS Conference, pp. 202–207 (2012)

    Google Scholar 

  • Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 148–152. AAAI Press (2014)

    Google Scholar 

  • Cambria, E., Poria, S., Gelbukh, A., Kwok, K.: Sentic API. A common-sense based api for concept-level sentiment analysis. In: 4th Workshop on Making Sense of Microposts (#Microposts2014), co-located with the 23rd International World Wide Web Conference (WWW 2014). CEUR Workshop Proceedings, vol. 1141, pp. 19–24 (2014)

    Google Scholar 

  • Cambria, E., Poria, S., Gelbukh, A., Thelwall, M.: Sentiment analysis is a big suitcase. IEEE Intell. Syst. 32(6), 74–80 (2017)

    Article  Google Scholar 

  • Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., Siong, C.E.: Modelling public sentiment in Twitter: using linguistic patterns to enhance supervised learning. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 49–65. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_4

    Chapter  Google Scholar 

  • Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A.Y.A., Cambria, E.: PerSent: a freely available Persian sentiment lexicon. In: Liu, C.-L., Hussain, A., Luo, B., Tan, K.C., Zeng, Y., Zhang, Z. (eds.) BICS 2016. LNCS (LNAI), vol. 10023, pp. 310–320. Springer, Cham (2016a). https://doi.org/10.1007/978-3-319-49685-6_28

    Chapter  Google Scholar 

  • Dashtipour, K., et al.: Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn. Comput. 8(4), 757–771 (2016b)

    Article  Google Scholar 

  • Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., Durrani, T.: A comparative study of persian sentiment analysis based on different feature combinations. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds.) CSPS 2017. LNEE, vol. 463, pp. 2288–2294. Springer, Singapore (2017a). https://doi.org/10.1007/978-981-10-6571-2_279

    Chapter  Google Scholar 

  • Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., Hussain, A.: Persian named entity recognition. In: 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), pp. 79–83. IEEE (2017b)

    Google Scholar 

  • Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., Hussain, A.: Exploiting deep learning for persian sentiment analysis. arXiv preprint arXiv:1808.05077 (2018)

  • Ghassemzadeh, H., Mojtabai, R., Karamghadiri, N., Ebrahimkhani, N.: Psychometric properties of a Persian-language version of the beck depression inventory-second edition: BDI-II-PERSIAN. Depress. Anxiety 21(4), 185–192 (2005)

    Article  Google Scholar 

  • Gholamain, M., Geva, E.: Orthographic and cognitive factors in the concurrent development of basic reading skills in English and Persian. Lang. Learn. 49(2), 183–217 (1999)

    Article  Google Scholar 

  • Ghosh, S., Ghosh, S., Das, D.: Part-of-speech tagging of code-mixed social media text. EMNLP 2016, 90–98 (2016)

    Google Scholar 

  • Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013)

    Article  Google Scholar 

  • Khallash, M., Hadian, A., Minaei-Bidgoli, B.: An empirical study on the effect of morphological and lexical features in Persian dependency parsing. In: 4th Workshop on Statistical Parsing of Morphologically Rich Languages, pp. 97–101 (2013)

    Google Scholar 

  • Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the omg! Icwsm 11(3), 538–541 (2011)

    Google Scholar 

  • Kumar, A., Sebastian, T.M.: Sentiment analysis: a perspective on its past, present and future. Int. J. Intell. Syst. Appl. IJISA 4, 1 (2012)

    Google Scholar 

  • Li, S., Huang, C.-R., Zhou, G., Lee, S.Y.M.: Employing personal/impersonal views in supervised and semi-supervised sentiment classification. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 414–423 (2010)

    Google Scholar 

  • Liu, B.: Opinion mining and sentiment analysis. In: Web Data Mining. pp. 459–526. Springer (2011)

    Google Scholar 

  • Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining Text Data, pp. 415–463. Springer (2012)

    Google Scholar 

  • Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)

    Article  Google Scholar 

  • Martineau, J., Finin, T.: Delta TFIDF: an improved feature space for sentiment analysis. ICWSM 9, 106–111 (2009)

    Google Scholar 

  • Musto, C., Semeraro, G., Polignano, M.: A comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts. Inf. Filter. Retr. p. 59 (2014)

    Google Scholar 

  • Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 128–135 (2008)

    Article  Google Scholar 

  • 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, vol. 10. Association for Computational Linguistics, pp. 79–86 (2002)

    Google Scholar 

  • Pérez-Rosas, V., Mihalcea, R.: Sentiment analysis of online spoken reviews. In: Inter-Speech, pp. 862–866 (2013)

    Google Scholar 

  • Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)

    Article  Google Scholar 

  • Poria, S., Cambria, E., Ku, L.W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: 2nd Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 28–37 (2014)

    Google Scholar 

  • Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Di Iorio, A., Di Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 41–47. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12024-9_5

    Chapter  Google Scholar 

  • Priyanka, C., Gupta, D.: Identifying the best feature combination for sentiment analysis of customer reviews, In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 102–108. IEEE (2013)

    Google Scholar 

  • Rahate, R.S., Emmanuel, M.: Feature selection for sentiment analysis by using SVM. Int. J. Comput. Appl. 84, 24–32 (2013)

    Google Scholar 

  • Raina, P.: Sentiment analysis in news articles using sentic computing. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pp. 959–962. IEEE (2013)

    Google Scholar 

  • Savoy, J.: Light stemming approaches for the French, Portuguese, German and Hungarian languages. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 103–113. ACM (2006)

    Google Scholar 

  • Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews and blog posts. In: 2013 IEEE 3rd International Advance Computing Conference (IACC), pp. 893–898. IEEE (2013)

    Google Scholar 

  • Sharifloo, A.A., Shamsfard, M.: A bottom up approach to Persian stemming. In: IJCNLP, pp. 583–588 (2008)

    Google Scholar 

  • Shukla, H., Kakkar, M.: Keyword extraction from educational video transcripts using NLP techniques. In: 2016 6th International Conference Cloud System and Big Data Engineering (Confluence), pp. 105–108. IEEE (2016)

    Google Scholar 

  • Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the 2000 Joint SIGDAT Conference on Empirical methods in Natural Language Processing and Very Large Corpora: Held in Conjunction with the 38th Annual Meeting of the Association for Computational Linguistics, vol. 13, pp. 63–70 (2000)

    Google Scholar 

  • Tanawongsuwan, P.: Product review sentiment classification using part-of-speech. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 424–427. IEEE (2010)

    Google Scholar 

  • Vaziripour, E., Giraud-Carrier, C., Zappala, D.: Analyzing the political sentiment of Tweets in Farsi. In: 10th International AAAI Conference on Web and Social Media, pp. 669–702 (2016)

    Google Scholar 

  • Windfuhr, G., Perry, J.R.: Persian and Tajik. The Iranian Languages, pp. 416–544 (2009)

    Google Scholar 

  • Xu, Y., Jones, G.J., Li, J., Wang, B., Sun, C.: A study on mutual information-based feature selection for text categorization. J. Comput. Inf. Syst. 3, 1007–1012 (2007)

    Google Scholar 

  • Zhai, Z., Xu, H., Jia, P.: An empirical study of unsupervised sentiment classification of Chinese reviews. Tsinghua Sci. Technol. 15, 702–708 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing Science and Mathematics, University of Stirling, FK9 4LA, Stirling, Scotland, UK

    Kia Dashtipour & Amir Hussain

  2. CIC, Instituto Politécnico Nacional, 07738, Mexico City, Mexico

    Alexander Gelbukh

Authors
  1. Kia Dashtipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Gelbukh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Gelbukh .

Editor information

Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dashtipour, K., Hussain, A., Gelbukh, A. (2018). Adaptation of Sentiment Analysis Techniques to Persian Language. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77116-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77115-1

  • Online ISBN: 978-3-319-77116-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation