Ontology-Based Sentiment Analysis of Kazakh Sentences | SpringerLink
Skip to main content
Springer Nature Link
Log in

Ontology-Based Sentiment Analysis of Kazakh Sentences

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10406))

Included in the following conference series:

Abstract

Sentiment analysis one of the important and interesting task in natural languages. A number of resources and tools have been developed for sentiment analysis of English, Turkish, Russian and other languages. Unfortunately, there was no data and tools available for sentiment analysis in Kazakh. The Dictionary of Kazakh sentiment words has been created during the study. In this work we described the rule-based method using dictionary for sentiment analysis of texts in the Kazakh language, based on the morphological rules and ontological model. Ontological model for rule extraction that determines sentiment was built. Our rule based method achieves 83% accuracy for simple sentences.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

References

  1. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)

    Google Scholar 

  2. Pang, B., Lee, L.: Opinion mining and sentiment analysis. In: Foundations and Trends® in Information Retrieval. Now Publishers (2008)

    Google Scholar 

  3. Loukachevitch, N.V., Chetviorkin, I.I.: Evaluating sentiment analysis systems in Russian. Artif. Intell. Decis. Mak. 1, 25–33 (2014). (in Russian)

    Google Scholar 

  4. Chetviorkin, I., Braslavskiy, P., Loukachevich, N.: Sentiment analysis track at ROMIP 2011. In: Proceedings of International Conference Dialog-2012, vol. 2, pp. 1–14 (2012)

    Google Scholar 

  5. Chetvirokin, I., Loukachevitch, N.: Sentiment analysis track at ROMIP 2012. In: Proceedings of International Conference Dialog-2013, vol. 2, pp. 40–50 (2013)

    Google Scholar 

  6. Chetviorkin, I., Loukachevitch, N.: Extraction of Russian sentiment lexicon for product meta-domain. In: Proceedings of COLING 2012, pp. 593–610 (2012)

    Google Scholar 

  7. Steinberger, J., Lenkova, P., Ebrahim, M., Ehrmann, M., Hurriyetogly, A., Kabadjov, M., Steinberger, R., Tanev, H., Zavarella, V., Vazquez, S.: Creating sentiment dictionaries via triangulation. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, ACL-HLT, pp. 28–36 (2011)

    Google Scholar 

  8. Akba, F., Uçan, A., Sezer, EA., Sever, H.: Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In: Proceedings of the 8th European Conference on Data Mining, pp. 180–184 (2014)

    Google Scholar 

  9. Yıldırım, E., Çetin, F., Eryiğit, G., Temel, T.: The impact of NLP on Turkish sentiment analysis. In: Proceedings of the TURKLANG 2014 International Conference on Turkic Language Processing, Istanbul (2014)

    Google Scholar 

  10. Eryiğit, G., Çetin, F., Yanık, M., Temel, T., Çiçekli, I.: TURKSENT: a sentiment annotation tool for social media. In: Proceedings of the 7th Linguistic Annotation Workshop & Interoperability with Discourse, ACL 2013, Sofia, Bulgaria (2013)

    Google Scholar 

  11. Sixto, J., Almeida, A., López-de-Ipiña, D.: An approach to subjectivity detection on Twitter using the structured information. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS, vol. 9875, pp. 121–130. Springer, Cham (2016). doi:10.1007/978-3-319-45243-2_11

    Chapter  Google Scholar 

  12. Mohammad, S., Salameh, M., Kiritchenko, S.: Sentiment lexicons for Arabic social media. In: Proceedings of the 10th Edition of the Language Resources and Evaluation Conference, Portorož, Slovenia (2016)

    Google Scholar 

  13. Tartir, S., Abdul-Nabi, I.: Semantic sentiment analysis in Arabic social media. J. King Saud Univ. Comput. Inf. Sci. 29(2), 229–233 (2016)

    Google Scholar 

  14. Sakenovich, N.S., Zharmagambetov, A.S.: On one approach of solving sentiment analysis task for Kazakh and Russian languages using deep learning. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS, vol. 9876, pp. 537–545. Springer, Cham (2016). doi:10.1007/978-3-319-45246-3_51

    Chapter  Google Scholar 

  15. Abdullin, Y.B., Ivanov, V.V.: Deep learning model for bilingual sentiment classification of short texts. Sci. Tech. J. Inf. Technol. Mech. Optics 17(1), 129–136 (2017)

    Google Scholar 

  16. Picard, R.W.: Affective computing. MIT Media Laboratory Perceptual Computing Section Technical Report No. 321. Media Lab. Massachusetts Institute of Technology, Cambridge Univ. (1995)

    Google Scholar 

  17. Biondi, G., Franzoni, V., Li, Y., Milani, A.: Web-based similarity for emotion recognition in web objects. In: Proceedings - 9th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2016, pp. 327–332 (2016)

    Google Scholar 

  18. Poria, S., Chaturvedi, I., Cambria, E., Hussain, A.: Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: Proceedings - IEEE International Conference on Data Mining, ICDM, art. no. 7837868, pp. 439–448 (2017)

    Google Scholar 

  19. Arunnehru, J., Kalaiselvi Geetha, M.: Automatic human emotion recognition in surveillance video. In: Dey, N., Santhi, V. (eds.) Intelligent Techniques in Signal Processing for Multimedia Security. SCI, vol. 660, pp. 321–342. Springer, Cham (2017). doi:10.1007/978-3-319-44790-2_15

    Chapter  Google Scholar 

  20. Jiang, R., Ho, A.T.S., Cheheb, I., Al-Maadeed, N., Al-Maadeed, S., Bouridane, A.: Emotion recognition from scrambled facial images via many graph embedding. Pattern Recogn. 67, 245–251 (2017)

    Article  Google Scholar 

  21. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  22. Yergesh, B., Mukanova, A., Sharipbay, A., Bekmanova, G., Razakhova, B.: Semantic hyper-graph based representation of nouns in the Kazakh language. Computacion y Sistemas 18(3), 627–635 (2014)

    Google Scholar 

  23. Cicortas, A., Iordan, V., Fortis, A.: Considerations on construction ontologies. J. Ann. Comput. Sci. Ser. 1, 79–88 (2009)

    Google Scholar 

  24. Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum Comput Stud. 43(5–6), 907–928 (1995)

    Article  Google Scholar 

  25. Yergesh, B., Sharipbay, A., Bekmanova, G., Lipnitskii, S.: Sentiment analysis of Kazakh phrases based on morphological rules. J. Kyrgyz State Tech. Univ. Named After I. Razzakov. Theor. Appl. Sci. Tech. J. 2(38), 39–42 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. L.N.Gumilyov Eurasian National University, Astana, Kazakhstan

    Banu Yergesh, Gulmira Bekmanova & Altynbek Sharipbay

  2. Kazakh University of Technology and Business, Astana, Kazakhstan

    Manas Yergesh

Authors
  1. Banu Yergesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gulmira Bekmanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Altynbek Sharipbay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manas Yergesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Banu Yergesh .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Yergesh, B., Bekmanova, G., Sharipbay, A., Yergesh, M. (2017). Ontology-Based Sentiment Analysis of Kazakh Sentences. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62398-6_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62397-9

  • Online ISBN: 978-3-319-62398-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation