Создание базы данных ключевых слов, для определения экстремистской направленности в веб-контенте на казахском языке
DOI:
https://doi.org/10.26577/jmmcs-2018-1-492Ключевые слова:
экстремистские тексты, частота терминов, классификация текстаАннотация
Данная работа является частью исследования создания моделей семантического анализа
для определения экстремистской направленности в тексте. Для решения данной задачи
была построена модель, которая состоит из пяти этапов: определение веб-сайтов
экстремистских групп, подготовка к извлечению данных, извлечение данных, анализ данных
и классификация. Данная работа представляет результаты этапа анализа данных указанной
модели. Целью исследования является определение ключевых слов, часто использующихся
экстремистами, которые в дальнейшем будут использоваться для классификации текстов
на «экстремистские» и «нейтральные» категорий с использованием методов машинного
обучения. Для казахского языка не существует такой базы данных. В результате этого
исследования был создан экспериментальный корпус и список ключевых слов на казахском
языке. Ключевые слова были добавлены в базу данных с различными морфологическими
вариантами. Была разработана программа, которая проверяет входной текст на наличие
экстремистских ключевых слов и возвращает найденные слова.
Библиографические ссылки
[1] Al-Rowaily Kh., Abulaish M., Haldar N.,Al-Rubaian M., "BiSAL – A bilingual sentiment analysis lexicon to analyze
Dark Web forums for cyber security," Digital Investigation: The International Journal of Digital Forensics & Incident
Response archive 14 (2015): 53–62.
[2] Ananyeva M. I., Deviatkin D. A., Kobozeva M. V., Smirnov I. V., "Lingvostatisticheskii analiz tekstov ekstremistskoi
napravlennosti [Lingua-statistic analysis of extremist texts]" (paper presented at the proceedings of International Conference
on Situational Centers and Information-Analytical System 4i Class for Monitoring and Security Tasks, Russia,
TsarGrad, November 21-24, 2015-2016).
[3] Azizan S.A., Aziz I.A., "Terrorism detection based on sentiment analysis using machine learning," Journal of Engineering
and Applied Sciences 12(3)(2017):691–698.
[4] Chen Hsinchun, Dark Web Exploring and Data Mining the Dark Side of the Web(Springer, 2012), 3–105.
[5] Cohen K.,Johansson F.,Kaati L. and Mork, Clausen J., "Detecting Linguistic Markers for Radical Violence in Social
Media, Terrorism and Political Violence," Terrorism and Political Violence 26(2014): 246–253.
[6] Devyatkin D.A., Smirnov V., Ananyeva V.I., Kobozeva M.V., "Exploring linguistic features for extremist texts detection
(on the material of Russian-speaking illegal texts)" (paper presented at 2017 IEEE International Conference on Intelligence
and Security Informatics (ISI), Beijing, China, Jule 22-24, 2017).
[7] Enghin Omer, "Using machine learning to identify jihadist messages on Twitter" (Independent thesis, Uppsala University,
2015).
[8] Finlayson M.A., Halverson J.R., Corman S.R., "The N2 corpus: A semantically annotated collection of Islamist extremist
stories" (paper presented at the Proceedings of the Ninth International Conference on Language Resources and Evaluation
(LREC’14), Iceland, Reykjavik, May 26-31, 2014).
[9] "Google Online Translator." Accessed January 12, 2018, https://translate.google.com/#ru/kk/.
[10] Information Portal of Kazakhstan. "Okolo 500 kazakhstantsev voiuiut v Sirii i Irake na storone IGIL. [About 500 Kazakhstanis
are fighting in Syria and Iraq on the side of ISIS]." Accessed February 10, 2018, http://today.kz/news/mir/2017-
10-31/753466-okolo-500-kazahstantsev-voyuyut-v-sirii-i-irake-na-storone-igil/.
[11] "Integrated development environment Visual C." Accessed December 17, 2017, https://www.visualstudio.com.
[12] Johansson F., Kaati L., Sahlgren M., "Detecting Linguistic Markers of Violent Extremism in Online Environments,"
in Combating Violent Extremism and Radicalization in the Digital Era, ed. Khader, Majeed (Hershey PA: Information
Science Reference, 2016), 374–390.
[13] Scanlon J.R., "Automatic Detection and Forecasting of Violent Extremist Cyber-Recruitment" (Master thesis, University
of Virginia, 2014).
[14] Scrivens R., Frank R., "Sentiment Based on the classification of radical text on the Web" (paper presented at the
Proceedings of 2016 European Intelligence and Security Informatics Conference (EISIC), Uppsala, Sweden, August 17-19,
2016).
[15] "SQlite Expert Database." Accessed January 15, 2018, www.sqliteexpert.com.
[16] Targeir A., Perera S., "Mapping Extremist Forums using Text Mining" (Master thesis, University of Agder, 2013).
[17] Wikipedia. "tf–idf." Accessed November 12, 2017, https://ru.wikipedia.org/wiki/TF-IDF.
[18] Zhavoronkova T.V., "Ispol’zovanie seti Internet terroristicheskimi i ekstremistskimi organizatsiiami [Usage of the Internet
by terrorist and extremist organizations]." Orenburg State University bulletin 3 (178): 30.
Dark Web forums for cyber security," Digital Investigation: The International Journal of Digital Forensics & Incident
Response archive 14 (2015): 53–62.
[2] Ananyeva M. I., Deviatkin D. A., Kobozeva M. V., Smirnov I. V., "Lingvostatisticheskii analiz tekstov ekstremistskoi
napravlennosti [Lingua-statistic analysis of extremist texts]" (paper presented at the proceedings of International Conference
on Situational Centers and Information-Analytical System 4i Class for Monitoring and Security Tasks, Russia,
TsarGrad, November 21-24, 2015-2016).
[3] Azizan S.A., Aziz I.A., "Terrorism detection based on sentiment analysis using machine learning," Journal of Engineering
and Applied Sciences 12(3)(2017):691–698.
[4] Chen Hsinchun, Dark Web Exploring and Data Mining the Dark Side of the Web(Springer, 2012), 3–105.
[5] Cohen K.,Johansson F.,Kaati L. and Mork, Clausen J., "Detecting Linguistic Markers for Radical Violence in Social
Media, Terrorism and Political Violence," Terrorism and Political Violence 26(2014): 246–253.
[6] Devyatkin D.A., Smirnov V., Ananyeva V.I., Kobozeva M.V., "Exploring linguistic features for extremist texts detection
(on the material of Russian-speaking illegal texts)" (paper presented at 2017 IEEE International Conference on Intelligence
and Security Informatics (ISI), Beijing, China, Jule 22-24, 2017).
[7] Enghin Omer, "Using machine learning to identify jihadist messages on Twitter" (Independent thesis, Uppsala University,
2015).
[8] Finlayson M.A., Halverson J.R., Corman S.R., "The N2 corpus: A semantically annotated collection of Islamist extremist
stories" (paper presented at the Proceedings of the Ninth International Conference on Language Resources and Evaluation
(LREC’14), Iceland, Reykjavik, May 26-31, 2014).
[9] "Google Online Translator." Accessed January 12, 2018, https://translate.google.com/#ru/kk/.
[10] Information Portal of Kazakhstan. "Okolo 500 kazakhstantsev voiuiut v Sirii i Irake na storone IGIL. [About 500 Kazakhstanis
are fighting in Syria and Iraq on the side of ISIS]." Accessed February 10, 2018, http://today.kz/news/mir/2017-
10-31/753466-okolo-500-kazahstantsev-voyuyut-v-sirii-i-irake-na-storone-igil/.
[11] "Integrated development environment Visual C." Accessed December 17, 2017, https://www.visualstudio.com.
[12] Johansson F., Kaati L., Sahlgren M., "Detecting Linguistic Markers of Violent Extremism in Online Environments,"
in Combating Violent Extremism and Radicalization in the Digital Era, ed. Khader, Majeed (Hershey PA: Information
Science Reference, 2016), 374–390.
[13] Scanlon J.R., "Automatic Detection and Forecasting of Violent Extremist Cyber-Recruitment" (Master thesis, University
of Virginia, 2014).
[14] Scrivens R., Frank R., "Sentiment Based on the classification of radical text on the Web" (paper presented at the
Proceedings of 2016 European Intelligence and Security Informatics Conference (EISIC), Uppsala, Sweden, August 17-19,
2016).
[15] "SQlite Expert Database." Accessed January 15, 2018, www.sqliteexpert.com.
[16] Targeir A., Perera S., "Mapping Extremist Forums using Text Mining" (Master thesis, University of Agder, 2013).
[17] Wikipedia. "tf–idf." Accessed November 12, 2017, https://ru.wikipedia.org/wiki/TF-IDF.
[18] Zhavoronkova T.V., "Ispol’zovanie seti Internet terroristicheskimi i ekstremistskimi organizatsiiami [Usage of the Internet
by terrorist and extremist organizations]." Orenburg State University bulletin 3 (178): 30.
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Как цитировать
Bolatbek, M. A., Mussiraliyeva, S. Z., & Tukeyev, U. A. (2018). Создание базы данных ключевых слов, для определения экстремистской направленности в веб-контенте на казахском языке. Вестник КазНУ. Серия математика, механика, информатика, 97(1), 134–142. https://doi.org/10.26577/jmmcs-2018-1-492
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Информатика
