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Detection of Negation in the Serbian Language

Published: 25 June 2018 Publication History

Abstract

The number of documents written in the Serbian language is constantly increasing, hence the need for the computer processing of the same is increasingly relevant. It is impossible to process natural language without processing the individual phenomena which occur in the syntax of a language. One such phenomenon is negation, and its processing contributes to the improvement of the quality of document processing. In this paper, we present an algorithm for the detection of negation and the extent of negation that includes specific syntax rules. It is shown that at least 7.59% of the classical approach to negation processing and detection of its scope has been improved. We applied the proposed method for negation processing on the sentiment analysis of tweets and an improvement in the accuracy of sentiment determination of 9.66% was obtained.

References

[1]
Vuk Batanović, Boško Nikolić, and Milan Milosavljević. 2016. Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset. In LREC.
[2]
Henk Harkema, John N. Dowling, Tyler Thornblade, and Wendy W. Chapman. 2009. ConText: An Algorithm for Determining Negation, Experiencer, and Temporal Status from Clinical Reports. J. of Biomedical Informatics 42, 5 (Oct. 2009), 839--851.
[3]
S. M. Jimenez-Zafra, M. T. Martin Valdivia, E. Martinez Camara, and L. A. Urena-Lopez. 2017. Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter. IEEE Transactions on Affective Computing PP:99 (2017).
[4]
Jovana Kovačević and Jelena Graovac. 2015. Application of a Structural Support Vector Machine method to N-gram based text classification in Serbian. INFOtheca - Journal of Information and Library Science 16(1) (2015).
[5]
Miloš Kovačević. 2002. Sintaksička negacija u srpskome jeziku. Izdavačka jedinica Univerziteta u NiÅąu.
[6]
Cvetana Krstev, Gordana Pavlović-Lazetic, Duško Vitas, and Ivan Obradović. 2004. Using Textual and Lexical Resources in Developing Serbian Wordnet. Romanian Journal of Information Science and Technology 7(1-2) (2004), 147--161.
[7]
Adela Ljajić, Ulfeta Marovac, and Aldina Avdić. 2017. Processing of Negation in Sentiment Analysis for the Serbian Language. In IcETRAN 2017 Conference proceedingsAt. Serbia.
[8]
Miljana Mladenović, Jelena Mitrović, Cvetana Krstev, and Duško Vitas. 2015. Hybrid sentiment analysis framework for a morphologically rich language. Journal of Intelligent Information Systems 46 (2015), 599--620.
[9]
Pradeep G. Mutalik, Adwait Deshpande, and Prakash M. Nadkarni. 2001. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. Journal of the American Medical Informatics Association: JAMIA 8 6 (2001), 598--609.
[10]
Jelena Petković. 2009. Sintaksička negacija u svetlu matematičke i logičke negacije. In SAVREMENA PROUÄŇAVANJA JEZIKA I KNJIÅ¡EVNOSTI, Zbornik radova sa I naučnog skupa mladih filologa Srbije. Filološko-umetnički fakultet u Kragujevcu, Kragujevac, Srbia.
[11]
G. Petrović. 1990. Logika. Školska knjiga. Zagreb.
[12]
Rion Snow, Lucy Vanderwende, and Arul Menezes. 2006. Effectively Using Syntax for Recognizing False Entailment. In Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 33--40.
[13]
Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, and János Csirik. 2008. The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics 9 (2008), S9 - S9.
[14]
Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010. A Survey on the Role of Negation in Sentiment Analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing (NeSp-NLP '10). Association for Computational Linguistics, Stroudsburg, PA, USA, 60--68. http://dl.acm.org/citation.cfm?id=1858959.1858970
[15]
Xiaodan Zhu, Hongyu Guo, Saif Mohammad, and Svetlana Kiritchenko. 2014. An Empirical Study on the Effect of Negation Words on Sentiment. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Baltimore, Maryland, 304--313. http://www.aclweb.org/anthology/P14-1029

Cited By

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  • (2023)Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian LanguageApplied Sciences10.3390/app1313776013:13(7760)Online publication date: 30-Jun-2023
  • (2023)Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature ReviewConfluence of Artificial Intelligence and Robotic Process Automation10.1007/978-981-19-8296-5_5(95-131)Online publication date: 14-Mar-2023

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cover image ACM Other conferences
WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics
June 2018
398 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2018

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Author Tags

  1. Grammar Rules
  2. Natural language Processing
  3. Negation
  4. Serbian Language
  5. Text Mining

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WIMS '18

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

View all
  • (2023)Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian LanguageApplied Sciences10.3390/app1313776013:13(7760)Online publication date: 30-Jun-2023
  • (2023)Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature ReviewConfluence of Artificial Intelligence and Robotic Process Automation10.1007/978-981-19-8296-5_5(95-131)Online publication date: 14-Mar-2023

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