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(2016) 76 : 107. ⇒ 67 [4] R. N. Behera, R. Manan, S. Dash, Ensemble based hybrid machine learning approach for sentiment classification – A Review, International Journal of Computer Applications 146 , 6 (2016) 31–36. ⇒ 59 [5] S. Brody, N. Diakopoulos, Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs, Conference on Empirical Methods in Natural Language Processing , 2007, pp. 562–570. ⇒ 64 [6] Y. Choi, H.Lee, Data properties and the performance of sentiment classification for electronic commerce applications

with TF-IDF based Framework for Text Categorization. Procedia Engineering , 69 , 1356–1364. Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. Pacific- -Asia Conference on Knowledge Discovery and Data Mining , 201–213. Yan, B. Y. (2017). Microblog sentiment classification using parallel SVM in apache spark. 2017 IEEE International Congress on Big Data (BigData Congress) , IEEE, 282–288.

References [1] Abbasi, A., France, S., Zhang, Z., Chen, H.: Selecting Attributes for Sentiment Classification Using Feature Relation Networks. IEEE Transactions on Knowledge and Data Engineering, 23 (3), 447-462 (2011). [2] Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proc. of the Int. Conference on Language Resources and Evaluation (2010). [3] Blagus, R., Lusa, L.: SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14 (1), 1471–2105 (2013). [4] Blitzer

Abstract

Social networking sites such as Facebook, Twitter and VKontakte, online stores such as eBay, Amazon and Alibaba as well as many other websites allow users to share their thoughts with their peers. Often those thoughts contain not only factual information, but also users’ opinion and feelings. This subjective information may be extracted using sentiment analysis methods, which are currently a topic of active research. Most studies are carried out on the basis of texts written in English, while other languages are being less researched. The present survey focuses on research conducted on the sentiment analysis for the Latvian and Russian languages.

. Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches. – Expert Systems with Applications, Vol. 36 , 2009, No 3, pp. 6527-6535. 5. Virmani, D., V. Malhotra, R. Tyagi. Sentiment Analysis Using Collaborated Opinion Mining. – arXiv preprint arXiv:1401.2618., 2014. 6. Zadeh, L. A. Fuzzy Sets. – Information and Control, Vol. 8 , 1965, No 3, pp. 338-353. 7. Jebaseeli, A. N., E. Kirubakaran. Genetic Optimized Neural Network Algorithm to Improve Classification Accuracy for Opinion Mining of m-Learning Reviews. – IJETTCS, Vol

and Czech Corpus Linguistics , pages 54–73, Veda, Bratislava. [4] Moilanen, K. and Pulman, S. (2007). Sentiment composition. In: Proceedings of RANLP , pages 378–382, Borovets, Bulgaria. [5] Nakagawa, T., Kentaro, I., and Kurohashi, S. (2010). Dependency tree-based sentiment classification using CRFs with hidden variables. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics , Association for Computational Linguistics, Los Angeles, California, USA. [6] Rentoumi, V., Petrakis, S

classification and score the modifiers and negations using a set of positive and negative modifiers and negation list. Thirdly, they apply sentiment classification of words using SentiWordNet-based classifier. Fourthly, they detect the domain specific words and label them with correct sentiment class and score. Finally, they perform sentiment classification of reviews at sentence and review level. The experiments obtain classification results with improved accuracy, precision, recall and F-measure as compared to comparing methods. Regarding rule-based approaches, it is

-688. 36. Li, Q. Text Classification Based on SVM Network. – Electronic Technology, Vol. 10 , 2014, pp. 8-11. 37. Chen, P. W., X. F. Fu. Research on Sentiment Classification of Text Based on SVM. – Journal of Guangdong University of Technology, Vol. 31 , 2014, No 3, pp. 95-101. 38. Gao, X. W., S. Y. Zheng, L. Gao et al. WeChat Monitoring Research Based on SVM Active Learning. – Computer & Digital Engineering, Vol. 44 , 2016, No 4, pp. 715-719. 39. Li, Z. Y., X. W. Yang, M. Wang. Research on the Classification of Travel Demand Information and the Acquisition of