Sentiment analysis of the US and Ukrainian presidential speeches

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dc.citation.epage70
dc.citation.spage60
dc.contributor.affiliationApplied Linguistics Department, Lviv Polytechnic National University
dc.contributor.affiliationLviv, Ukraine
dc.contributor.authorDilai, Marianna
dc.contributor.authorOnukevych, Yuliya
dc.contributor.authorDilay, Iryna
dc.coverage.placenameLviv
dc.coverage.temporal25-27 June 2018
dc.date.accessioned2018-09-03T11:41:16Z
dc.date.available2018-09-03T11:41:16Z
dc.date.created2018-06-25
dc.date.issued2018-06-25
dc.description.abstractThe paper presents the results of the sentiment analysis of the US and Ukrainian presidential speeches. By means of SentiStrength and UAM Corpus Tool programs, we attempt to extract opinions and sentiments in the speeches of Donald Trump and Petro Poroshenko. The main contribution of this study is the adaptation of the SentiStrength program to the Ukrainian language by compiling a political domain glossary of the Ukrainian emotion-bearing words. Furthermore, we compare lexical means of sentiment expression in the analyzed texts.
dc.format.extent60-70
dc.format.pages11
dc.identifier.citationDilai M. Sentiment analysis of the US and Ukrainian presidential speeches / Marianna Dilai, Yuliya Onukevych, Iryna Dilay // Computational linguistics and intelligent systems, 25-27 June 2018. — Lviv : Lviv Polytechnic National University, 2018. — Vol 2 : Workshop. — P. 60–70. — (Part 1. Keynote speakers talks).
dc.identifier.citationenDilai M. Sentiment analysis of the US and Ukrainian presidential speeches / Marianna Dilai, Yuliya Onukevych, Iryna Dilay // Computational linguistics and intelligent systems, 25-27 June 2018. — Lviv : Lviv Polytechnic National University, 2018. — Vol 2 : Workshop. — P. 60–70. — (Part 1. Keynote speakers talks).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/42572
dc.language.isoen
dc.publisherLviv Polytechnic National University
dc.relation.ispartofComputational linguistics and intelligent systems (2), 2018
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dc.relation.references6. B. Pang & L. Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, Vol. 2, No 1-2 , 2008, 1–135.
dc.relation.references7. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol. 10, 2002,. 79–86.
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dc.relation.references10. L. Zhang, B. Liu, Aspect and Entity Extraction for Opinion Mining, in Data Mining and Knowledge Discovery for Big Data, 2014, Springer, Berlin, Heidelberg, 1–40.
dc.relation.references11. M. Lobur, A. Romaniuk, M. Romanyshyn, Defining an approach for deep sentiment analysis of reviews in Ukrainian, Visnyk Natsionalnogo Universytetu Lvivska Politehnika, No 747, Komputerni systemy proektuvannia, Teoria i praktyka, 2012, 124–130.
dc.relation.references12. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, & A. Kappas, Sentiment strength detection in short informal text, Journal of the American Society for Information Science and Technology, 61(12), 2012, 2544–2558.
dc.relation.referencesen1. G.E. Marcus, Emotions in politics, Annual Review of Political Science, 3, 2000, 221-50.
dc.relation.referencesen2. N. A. Valentino, T. Brader, E. W. Groenendyk, K. Gregorowicz & W. L. Hutchings, Election night’s alright for fighting: The role of emotions in political participation, Journal of Politics, 73, 2011, 156-170.
dc.relation.referencesen3. B. Albertson & S. K. Gadarian, Anxious Politics: Democratic Citizenship in a Threatening World, 2015, New York: Cambridge University Press,.
dc.relation.referencesen4. M. Lodge & C. Taber, Implicit affect for political candidates, parties, and issues: An experimental test of the hot cognition hypothesis, Political Psychology, 26, 2005, 455-482.
dc.relation.referencesen5. C. A. Smith & P.C. Ellsworth, Patterns of cognitive appraisal in emotion, Journal of Personality and Social Psychology, 48, 1985, 813-838.
dc.relation.referencesen6. B. Pang & L. Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, Vol. 2, No 1-2 , 2008, 1–135.
dc.relation.referencesen7. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol. 10, 2002,. 79–86.
dc.relation.referencesen8. E. Amigo, A. Corujo, J. Gonzalo, E. Meij, M. de Rijke, Overview of RepLab 2012: Evaluating Online Reputation Management Systems, CLEF 2012 Evaluation Labs and Workshop Notebook Papers, Rome.
dc.relation.referencesen9. B. Liu, Sentiment analysis and opinion mining, 2012, Morgan & Claypool Publishers.
dc.relation.referencesen10. L. Zhang, B. Liu, Aspect and Entity Extraction for Opinion Mining, in Data Mining and Knowledge Discovery for Big Data, 2014, Springer, Berlin, Heidelberg, 1–40.
dc.relation.referencesen11. M. Lobur, A. Romaniuk, M. Romanyshyn, Defining an approach for deep sentiment analysis of reviews in Ukrainian, Visnyk Natsionalnogo Universytetu Lvivska Politehnika, No 747, Komputerni systemy proektuvannia, Teoria i praktyka, 2012, 124–130.
dc.relation.referencesen12. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, & A. Kappas, Sentiment strength detection in short informal text, Journal of the American Society for Information Science and Technology, 61(12), 2012, 2544–2558.
dc.rights.holder© 2018 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectsentiment analysis
dc.subjectUkrainian sentiment lexicon
dc.subjectmanual annotation
dc.subjectpresidential speeches
dc.titleSentiment analysis of the US and Ukrainian presidential speeches
dc.typeConference Abstract

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