Ontology-based sentiment analysis of kazakh sentences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10406 LNCS, Issue , 2017, Pages 669-677

  Yergesh, Banu ^a   Bekmanova, Gulmira ^a   Sharipbay, Altynbek ^a   Yergesh, Manas ^b  

^a L N GUMILYOV EURASIAN NATIONAL UNIVERSITY   (Kazakhstan)

^b Kazakh University of Technology and Business   (Kazakhstan)

Author keywords

Classification; Kazakh language; Morphological rules; Ontology; Production rules; Rule based method; Sentiment analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING;

KAZAKH LANGUAGE; MORPHOLOGICAL RULES; PRODUCTION RULES; RULE-BASED METHOD; SENTIMENT ANALYSIS;

ONTOLOGY;

EID: 85026773691     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-62398-6_47     Document Type: Conference Paper

References (25)

1. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)
2. Pang, B., Lee, L.: Opinion mining and sentiment analysis. In: Foundations and Trends® in Information Retrieval. Now Publishers (2008)
3. Loukachevitch, N.V., Chetviorkin, I.I.: Evaluating sentiment analysis systems in Russian. Artif. Intell. Decis. Mak. 1, 25-33 (2014). (in Russian)
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)
5. Chetvirokin, I., Loukachevitch, N.: Sentiment analysis track at ROMIP 2012. In: Proceedings of International Conference Dialog-2013, vol. 2, pp. 40-50 (2013)
6. Chetviorkin, I., Loukachevitch, N.: Extraction of Russian sentiment lexicon for product meta-domain. In: Proceedings of COLING 2012, pp. 593-610 (2012)
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)
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)
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)
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)
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
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)
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)
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
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)
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)
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)
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)
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
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)
21. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267-307 (2011)
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)
23. Cicortas, A., Iordan, V., Fortis, A.: Considerations on construction ontologies. J. Ann. Comput. Sci. Ser. 1, 79-88 (2009)
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)
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)