Conflict matrix as a mechanism of identifying the conflict in emotions of written text

  • Authors

    • Abbasi Mohsin Manshad Udmurt state University, Izhevsk, Russian FederationUniversity of Azad Jammu & Kashmir, Muzaffarabad (A.K), Pakistan
    • Abbasi Anees Qumar
    • Beltiukov Anatoly Petrovich
    • Hussain Lal.
    2020-06-14
    https://doi.org/10.14419/ijet.v9i2.30664
  • Blog of Text, Conflict Matrix, Emotions, Precision, Recall.

  • Text is a major source of information and is considered as a mechanism of communicating emotions and ideas. The emotion and their analysis from written text gained popularity over recent decades. It has been credited to the growth of information technology and the rapid increase in availability of internet around the globe. In this work, the main idea is to identify the conflict in emotions that exist in the written text. The use of conflict analysis is to identify the contradictory views of people about an object or a topic of discussion. Its existence in text however complicates the process of analysis of emotions from text. This paper describes a mechanism in which the emotions in each pair of sentence are considered as conflicting to each other. The emotional orientation of each pair of sentence is observed to identify the truth-value of the proposed conflict hypothesis. The result of the analysis is summarized using the Conflict matrix. Conflict matrix is a major product of this research that is used to identify the conflicting emotions in text and to measure their characteristics. The results of the experiment were analyzed using the supervised learning techniques along with the Confusion matrix. In methodology and conclusion sections of the paper, the results are discussed in detail.

     

     

  • References

    1. [1] Schrodt, PA (1984), Artificial intelligence and international crisis: An application of pattern recognition, In Annual meeting of the international studies association, Washington, DC, Connecticut: International Studies Association.

      [2] Schrodt, PA (1987), Classification of interstate conflict outcomes using a bootstrapped CLS algorithm, In Annual Meeting of the International Studies Association, Washington, DC, Connecticut: International Studies Association.

      [3] Schrodt, PA (1990), Predicting interstate conflict outcomes using a bootstrapped ID3 algorithm, Political Analysis 2, 31–56. https://doi.org/10.1093/pan/2.1.31.

      [4] Schrodt, P A., “Prediction of interstate conflict outcomes using a neural networkâ€, Social Science Computer Review, Vol. 9, No. 3 (1991), pp. 359–380. https://doi.org/10.1177/089443939100900302.

      [5] Schrodt, PA (1997), Early warning of conflict in Southern Lebanon using Hidden Markov Models. In Annual meeting of the international studies association, Washington, DC. Connecticut: International Studies Association.

      [6] Weber RP., “Computer-aided content analysis: A short primerâ€. Qualitative Sociology, Vol. 7, No.12, (1984), pp. 126–147. https://doi.org/10.1007/BF00987112.

      [7] Trappl R, Fürnkranz J, Petrak J, “Digging for peace: Using machine learning methods for assessing international conflict databasesâ€, In W. Wahlster (ed.) Proceedings of the 12th European conference on Artificial Intelligence, Chichester: Wiley, (1996), 453-57.

      [8] Trappl R, Fürnkranz J, Petrak (1997), Machine learning and case-based reasoning: Their potential role in preventing the outbreak of wars or in ending them. In G. https://doi.org/10.1007/978-3-7091-2668-4_13.

      [9] Gerner DJ, Schrodt PA., “Cluster-based early warning indicators for political change in the contemporary Levantâ€, American Political Science Review, Vol. 94, No. 4, (2000), pp. 803–818. https://doi.org/10.2307/2586209.

      [10] Wolfe AW, “Network thinking in peace and conflict studiesâ€, Peace and Conflict Studies, Vol. 11, No. 1, (2004), pp. 4-10. https://doi.org/10.1207/s15327949pac1101_6.

      [11] Hammarstr M, Heldt B, “The diffusion of military intervention: Testing a network position approachâ€, International Interactions, Vol. 28, No. 4, (2002), pp. 355–377. https://doi.org/10.1080/03050620214758.

      [12] Takács K (2002), Social network and intergroup conflict, PhD thesis, University of Groningen.

      [13] Kramer CR (2017), Network theory and violent conflicts, Basingstoke, Palgrave Macmillan. https://doi.org/10.1007/978-3-319-41393-8.

      [14] Perliger A, Pedahzur A, “Social network analysis in the study of terrorism and political violenceâ€, Political Science and Politics, Vol. 44, No. 1, (2011), pp. 45–50. https://doi.org/10.1017/S1049096510001848.

      [15] Deutschmann, E, “Between collaboration and disobedience. The behavior of the Guantánamo detainees and its consequencesâ€, Journal of Conflict Resolution, Vol. 60, No. 3, (2016), pp. 555–582. https://doi.org/10.1177/0022002714545331.

      [16] Gleditsch KS, Metternich NW, Ruggeri A, “Data and progress in peace and conflict researchâ€, Journal of Peace Research, Vol. 51, No. 3, (2014), pp. 301–314. https://doi.org/10.1177/0022343313496803.

      [17] Trappl R, Hörtnagl E, Rattenberger J, Schwank N, Bercovitch J, “Machine learning methods for better understanding, resolving, and preventing international conflictsâ€, In R. Trappl (ed.) Programming for peace, Dordrecht: Springer, Vol. 2, (2006), pp. 251-318 https://doi.org/10.1007/1-4020-4390-2_11.

      [18] Fischer PD, Schwemmer C, Fischbach K., “Collective sense making in times of crisis: Connecting terror management theory with twitter reactions to the berlin terrorist attackâ€, Computers in Human Behavior, Vol. 100, (2019), pp. 138–151. https://doi.org/10.1016/j.chb.2019.05.012.

      [19] Leschke JC, Schwemmer C (2019), Media bias towards African Americans before and after the Charlottesville rally, In Weizenbaum conference (p. 10), DEU.

      [20] Beltiukov AP, Abbasi MM, “Logical analysis of Emotions in Text from Natural languageâ€, Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, ИжевÑк, Vol.1, No. 29, (2019), pp. 106-116. https://doi.org/10.20537/vm190110.

      [21] Abbasi MM, Beltiukov AP, “Analyzing emotions from text corpus using word space CSIT`2018â€, Proceedings of the 20th International Workshop on Computer Science and Information Technologies ,Varna- Bulgaria, Industry 4.0, Vol. 3, No. 4, (2018), pp.161-164.

      [22] Abbasi MM, Beltiukov AP, “Identifying the strength of emotions in relation with the topic of text using Word spaceâ€, Proceedings of the 21th international workshop on computer science and information technologies, Austria, Vienna, Journal of Atlantis Highlights in Computer Sciences, Vol. 3, (2019), pp. 1-5. https://doi.org/10.2991/csit-19.2019.1.

      [23] Abbasi MM, Beltiukov AP, Hussain Lal, Abbasi AQ, “Analysis of emotions from texts for managing societyâ€, Infocommunication technologies Journal, Academy of Telecommunications and Informatics, Samara, Vol. 2, No.17, (2019), pp. 246-254. https://doi.org/10.18469/ikt.2019.17.2.15.

      [24] Abbasi MM, Beltiukov AP, “Summarizing emotions from text using Plutchik wheel of emotionâ€, 7th All Russian Conference on Information technology for intelligent decision-making support (ITIDS), Ufa, Russian Federation, Vol. 166, (2019), pp. 291-294.

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  • How to Cite

    Mohsin Manshad, A., Anees Qumar, A., Anatoly Petrovich, B., & Lal., H. (2020). Conflict matrix as a mechanism of identifying the conflict in emotions of written text. International Journal of Engineering & Technology, 9(2), 541-545. https://doi.org/10.14419/ijet.v9i2.30664