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Conflict analysis for Turkish debates using text mining and text segmentation techniques
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Apr 16, 2017
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Abstract
Conflict Analysis is one of the most challenging issues in the world that many organizations and governments try to carry out perfectly. It is crucial to have a correct analysis to prepare a resolution for a problem. Thus, this study paper focuses on the ways that a software program can detect the reasons of arguments in a debate. The examples of debate dialogs are chosen from Turkish language because there is not much research in this area with this language. Moreover, the techniques which are applied in this work can also be applied to other languages, because a sentiment word dictionary is used and sentiments are almost the same in every language. This is a prepared dictionary from SentiWordNet with all the sentiment points for English words. It is translated and extended for the Turkish language. Furthermore, both machine learning and lexicon-based approaches are implemented in order to increase the diversity of results. This paper aims to show that languages can be processed in a technical manner and meanings can be extracted from sentences to understand the reasons of arguments. Likewise, the main contribution of this study is that conflict analysis for Turkish debates can be applied with the techniques which are examined here and they are also suitable for other languages.Conflict Analysis is one of the most challenging issues in the world that many organizations and governments try to carry out perfectly. It is crucial to have a correct analysis to prepare a resolution for a problem. Thus, this study paper focuses on the ways that a software program can detect the reasons of arguments in a debate. The examples of debate dialogs are chosen from Turkish language because there is not much research in this area with this language. Moreover, the techniques which are applied in this work can also be applied to other languages, because a sentiment word dictionary is used and sentiments are almost the same in every language. This is a prepared dictionary from SentiWordNet with all the sentiment points for English words. It is translated and extended for the Turkish language. Furthermore, both machine learning and lexicon-based approaches are implemented in order to increase the diversity of results. This paper aims to show that languages can be processed in a technical manner and meanings can be extracted from sentences to understand the reasons of arguments. Likewise, the main contribution of this study is that conflict analysis for Turkish debates can be applied with the techniques which are examined here and they are also suitable for other languages.
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Conflict analysis for Turkish debates using text mining and text segmentation techniques. (2017). Global Journal of Computer Sciences: Theory and Research, 6(2), 19–25. https://doi.org/10.18844/gjcs.v6i2.1651
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References
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[36] Blunsom, P. Hidden Markov Models. Melbourne: The University of Melbourne, 2014.
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[38] Learn Turkish with Manisa Turkish. Basic Grammar. Istanbul: SIGN, 2016. Available from: http://www.turkishlanguage.co.uk/
[39] SentiWordNet database, 23 July 2016. Available from: http://sentiwordnet.isti.cnr.it/
[40] Solutions to Diagnostic Testing Practice Exercises, 2016. http://sites.stat.psu.edu/~lsimon/stat250/sp00/solutions/misc/diagtests.html
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[2] Ertav, B. Turkce dilbilgisi notlari: ygs – lys’ler icin ders notlari, 2014, pp 20.
[3] Balijepalli, S. Information retrieval based on sentiment analysis. VDM Publishin, 2008.
[4] Agarwal, B. and Mittal, N. Prominent feature extraction for sentiment analysis. Springer, 2015, pp. 13.
[5] Gokalp, M. Turk dili bilgisi. Sark Press, 1976, pp 157.
[6] Kagan, V., Rossini, E. and Sapounas, D. Sentiment analysis for ptsd signals. Springer Science & Business Media, 2013, pp. 23.
[7] Akdeniz, S. Turkcede yeni gelismeler. Ozek Press, 1973, pp 56.
[8] Tkalcic M., De Carolis, B., De Gemmis, M., Odic, A. and Kosir, A. Emotions and personality in personalized services: models, evaluation and applications. Springer, 2016, pp. 134.
[9] Gulsevin, G., & Boz E. Turkcenin cagdas sorunlari. Divan Press, 2006, pp 78.
[10] Liu, B. Sentiment analysis and opinion mining. Morgan & Claypool Publishers, 2012, pp 22.
[11] Stuckenschmidt, H. and Jannach, D. E-commerce and web technologies: 16th international conference on electronic commerce and web Technologies, ec-web 2015. Valencia: Springer, 2015, 239, pp. 109.
[12] Liu, B. Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press, 2015
[13] Pang, B. and Lee, L. Opinion mining and sentiment analysis. Now Publishers Inc, 2008, pp 26.
[14] Coleman, S. and Freelon, D. Handbook of digital politics. Edward Elgar Publishing, 2015, pp. 465.
[15] Ozaydın, U. Temiz turkce kilavuzu: iletisim guzeldir. Nubuto Limited Press, 2015, pp 29.
[16] Mandal, D., Kar, R., Das, S. and Panigrahi, B. K. Intelligent computing and applications: proceedings of the international conference on ica. Springer, 2015, 343, pp. 368.
[17] Bhattacherjee, A. and Fitzgerald, B. Shaping the future of ict research: methods and approaches: ifip wg 8.2 working conference.Tampa: Springer, 2012, 13-14, pp. 3.
[18] Management Association Information Resources. Enterprise resource planning: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications. IGI Global, 2013, pp. 1505.
[19] Goker, O. Uygulamali turkce bilgileri. Ankara: The Ministry of Education, 1997, 3, pp 95.
[20] Pierson, L. Data science for dummies. John Wiley & Sons, 2015, pp. 277.
[21] Sobaci, M. Z. Social media and local governments: theory and practice. Springer, 2015, pp. 121.
[22] Kutlu, M. Turk dili ve edebiyati ansiklopedisi: devirler, isimler, eserler, terimler, Dergah Press, 1977, 7, pp 119.
[23] Marcus, A. Design, User Experience, and Usability: User Experience Design for Diverse Interaction Platforms and Environments: Third International Conference, DUXU 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceeding. Springer, 2014, 2, pp. 487.
[24] Ishikiriyama, C. S., Miro, D. and Gomes, C. F. S. Text Mining Business Intelligence: a small sample of what words can say. Procedia Computer Science, 2015, 55, 261 – 267.
[25] Medhat, W., Hassan, A. and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 2015, 5, 1093 – 1113.
[26] Singh, P. K. and Husain, M. S. Methodological Study of Opinion Mining and Sentiment Analysis Techniques. International Journal on Soft Computing (IJSC), 2014, 5 (1).
[27] Cambria, E., Schuller, B., Xia, Y. and Havasi, C. New Avenues in Opinion Mining and Sentiment Analysis. IEEE Computer Society, 2013.
[28] Pang, B. and Lee, L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2008, 2 (1-2), pp. 4 - 5.
[29] Istanbul University Faculty of Literature. Turk dili ve edebiyatı dergisi. Istanbul: Istanbul University Faculty of Literature Press, 2006, 34, pp. 44.
[30] Turk Dil Kurumu. Turk dili arastırmaları yıllıgı 1. bolum. Turkish Language Association, 2006, pp. 80.
[31] Balijepalli, S. Blogvox2: a modular domain independent sentiment analysis system (Thesis for M.S. Degree). Maryland: University of Maryland.
[32] Demirel, O. Yapica Olumlu Anlamca Olumsuz Cumleler Hakkinda Bir Degerlendirme. A research about positive and negative sentences. Istanbul: Istanbul University, 2009
[33] Demirel, O. Turkiye Turkcesinde Olumsuz Cumle Yapıları (Thesis for M.A).Degree. Istanbul: Inonu University, 2002
[34] Ilhan, N. Turkcede Olumsuzluk. A research about the negative sentences in Turkish. Elazig: Firat University, 2007.
[35] Ucan, A. Automatic Sentiment Dictionary Translation and Using in Sentiment Analysis (Thesis for M.A. Degree). Ankara: Hacettepe University, 2014.
[36] Blunsom, P. Hidden Markov Models. Melbourne: The University of Melbourne, 2014.
[37] Steels, L., Kaplan, F., McIntyre, A. and Van Looveren, J. Crucial factors in the origins of word-meaning. Paris: Sony Computer Science Laboratory, 2000.
[38] Learn Turkish with Manisa Turkish. Basic Grammar. Istanbul: SIGN, 2016. Available from: http://www.turkishlanguage.co.uk/
[39] SentiWordNet database, 23 July 2016. Available from: http://sentiwordnet.isti.cnr.it/
[40] Solutions to Diagnostic Testing Practice Exercises, 2016. http://sites.stat.psu.edu/~lsimon/stat250/sp00/solutions/misc/diagtests.html
[41] Positivism Research Philosophy, 31 August 2016. Available from: http://research-methodology.net/research-philosophy/positivism/
[42] Deductive Approach, 2016.Available from: http://research-methodology.net/research-methodology/research-approach/deductive-approach-2/
[43] Inductive Approach, 2016. Available from: http://research-methodology.net/research-methodology/research-approach/inductive-approach-2/
[44] Research Design, 2016. Available from: http://research-methodology.net/research-methodology/research-design/
[45] Research Methods, 2016. Available from: http://research-methodology.net/research-methods/
[46] Convenience Sampling, 2016. Available from: http://research-methodology.net/sampling-in-primary-data-collection/convenience-sampling/