Sentiment Analysis in Latvian and Russian: A Survey

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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.

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  • [1] K. Ravi and V. Ravi “A survey on opinion mining and sentiment analysis: Tasks approaches and applications” Knowledge-Based Systems vol. 89 pp. 14–46 2015. https://doi.org/10.1016/j.knosys.2015.06.015

  • [2] Thomson Reuters “Thomson Reuters Adds Unique Twitter and News Sentiment Analysis to Thomson Reuters Eikon” [Online]. Available: https://www.thomsonreuters.com/en/press-releases/2014/thomson-reuters-adds-unique-twitter-and-news-sentiment-analysisto-thomson-reuters-eikon.html. [Accessed: Mar.8 2018].

  • [3] L. Chen G. Chen and F. Wang “Recommender Systems Based on User Reviews : The State of the Art” Systematic Reviews 2015 vol. 4 no. 5 2015.https://doi.org/10.1007/s11257-015-9155-5

  • [4] A. Ceron “Enlightening the voters: The effectiveness of alternative electoral strategies in the 2013 Italian election monitored through (sentiment) analysis of Twitter posts” European Consortium for Political Research pp. 1–25 2013.

  • [5] W. Medhat A. Hassan and H. Korashy “Sentiment analysis algorithms and applications: A survey” Ain Shams Engineering Journal vol. 5 pp. 1093–1113 2014. https://doi.org/10.1016/j.asej.2014.04.011

  • [6] S. Vohra and J. Teraiya “Applications and Challenges for Sentiment Analysis : A Survey” International Journal of Engineering Research and Technology vol. 2 no. 2 pp. 1–6 2013.

  • [7] V. A. Kharde and S. S. Sonawane “Sentiment Analysis of Twitter Data: A Survey of Techniques” International Journal of Computer Applications vol. 139 no. 11 pp. 5–15 2016. https://doi.org/10.5120/ijca2016908625

  • [8] K. Dashtipour S. Poria A. Hussain E. Cambria A. Y. A. Hawalah A. Gelbukh and Q. Zhou “Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques” Cognitive Computation vol. 8 pp. 757–771 2016. https://doi.org/10.1007/s12559-016-9415-7

  • [9] M. Korayem K. Aljadda and D. Crandall “Sentiment/subjectivity analysis survey for languages other than English” Social network analysis and mining vol. 6 no. 1 pp. 1–28 2016. https://doi.org/10.1007/s13278-016-0381-6

  • [10] “Datasets - Linked Data Models for Emotion and Sentiment Analysis Community Group.” [Online]. Available: https://www.w3.org/community/sentiment/wiki/Datasets. [Accessed: Mar. 8 2018].

  • [11] A. Esuli and F. Sebastiani “SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining” Proceedings of 5th Conference Language Resources and Evaluation pp. 417–422 2006.

  • [12] “MPQA Resources.” [Online]. Available: http://mpqa.cs.pitt.edu/. [Accessed: Mar. 8 2018].

  • [13] G. Garkaje E. Zilgalve and R. Dargis “Normalization and Automatized Sentiment Analysis of Contemporary Online Latvian Language” Frontiers in Artificial Intelligence and Applications vol. 268 pp. 83–86 2014. https://doi.org/10.3233/978-1-61499-442-8-83

  • [14] J. Peisenieks “Mašīntulkošanas iespējas Twitter sīkziņu sentimenta analīzē” B. S. thesis University of Latvia Latvia 2014.

  • [15] J. Peisenieks and R. Skadiņš “Uses of Machine Translation in the Sentiment Analysis of Tweets” Frontiers in Artificial Intelligence and Applications vol. 268 pp. 126–131 2014.

  • [16] J. Peisenieks “Latvian tweet sentiment corpus.” [Online]. Available: https://github.com/FnTm/latvian-tweet-sentiment-corpus. [Accessed: Mar. 10 2018].

  • [17] D. Nicmanis “Sabiedrības attieksmes modelēšana izmantojot sentimenta analīzi” B.S. thesis University of Latvia Latvia 2017.

  • [18] D. Nicmanis “LV twitter sentiment corpus.” [Online]. Available: https://github.com/nicemanis/LV-twitter-sentiment-corpus. [Accessed: 10-Mar-2018].

  • [19] K. Gediņš “Automātiskā teksta emocionālās noskaņas noteikšana latviešu valodā” B. S. thesis University of Latvia Latvia 2013.

  • [20] G. Špats and I. Birzniece “Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach” Complex Systems Informatics and Modeling Quarterly no. 7 pp. 51–59 2016. https://doi.org/10.7250/csimq.2016-7.03

  • [21] “Latvian positive and negative sentiment words.” [Online]. Available: https://github.com/pumpurs/SentimentWordsLV. [Accessed: Mar. 10 2018].

  • [22] G. Špats “Resources for opinion mining for written content classification in Latvian text.” [Online]. Available: https://github.com/gatis/om/tree/master/lexicon. [Accessed: Mar. 10 2018].

  • [23] N. Loukachevitch and Y. Rubtsova “Entity-Oriented Sentiment Analysis of Tweets: Results and Problems Natalia” Proceedings of the 18th International Conference on Text Speech and Dialogue Lecture Notes in Computer Science vol. 9302 pp. 551–559 2015. https://doi.org/10.1007/978-3-319-24033-6_62

  • [24] N. V. Loukachevitch and I. I. Chetviorkin “Open evaluation of sentiment-analysis systems based on the material of the Russian language” Scientific and Technical Information Processing vol. 41 no. 6 pp. 370–376 2014. https://doi.org/10.3103/S0147688214060057

  • [25] N. S. Sakenovich and A. S. Zharmagambetov “On One Approach of Solving Sentiment Analysis Task for Kazakh and Russian Languages Using Deep Learning” Computational Collective Intelligence Lecture Notes in Computer Science vol. 10449 pp. 537–545 2017. https://doi.org/10.1007/978-3-319-45246-3_51

  • [26] E. Tutubalina and S. Nikolenko “Constructing Aspect-Based Sentiment Lexicons with Topic Modeling” Proceedings of 5th Conference on Analysis of Images Social Networks and Text vol. 10716 pp. 208–220 2017. https://doi.org/10.1007/978-3-319-52920-2_20

  • [27] N. V. Loukachevitch P. D. Blinov E. V. Kotelnikov Y. V. Rubtsova V. V. Ivanov and E. V. Tutubalina “SentiRuEval: Testing Objectoriented sentiment analysis systems in Russian” Computational Linguistics and Intellectual Technologies vol. 2 no. 14 pp. 3–15 2015.

  • [28] G. Shalunts and G. Backfried “SentiSAIL: Sentiment Analysis in English German and Russian” Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition vol. 9166 2015. https://doi.org/10.1007/978-3-319-21024-7_6

  • [29] R. Galinsky A. Alekseev and S. Nikolenko “Improving Neural Network Models for Natural Language Processing in Russian with Synonyms” Proceedings of AINL FRUCT 2016 Conference vol. 3 pp. 45–51 2016.

  • [30] V. Bobichev O. Kanishcheva and O. Cherednichenko “Sentiment analysis in the Ukrainian and Russian news” Proceedings of 2017 IEEE 1st Electrical and Computer Engineering UKRCON 2017 pp. 1050–1055 2017. https://doi.org/10.1109/UKRCON.2017.8100410

  • [31] I. Gulbinskis “Digitālo tekstu sentimenta analīze” B. S. thesis University of Latvia Latvia 2010.

  • [32] “SemTi-Kamols.” [Online]. Available: http://www.semti-kamols.lv/?sadala=220. [Accessed: Mar. 10 2018].

  • [33] P. D. Turney “Thumbs up or thumbs down? Semantic Orientation applied to Unsupervised Classification of Reviews” Proceedings of the 40th Annual Meeting on Association for Computational Linguistics no. July pp. 417–424 2002.

  • [34] “Latviešu valodas tekstu korpuss.” [Online]. Available: http://www.korpuss.lv/. [Accessed: Mar. 8 2018].

  • [35] P. Paikens “Latvian morphology module.” [Online]. Available: https://github.com/PeterisP/morphology. [Accessed: Mar. 8 2018].

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