PL EN


Preferences help
enabled [disable] Abstract
Number of results
2018 | 113 | 227-234
Article title

Mass Violence Detection Using Data Mining Techniques

Content
Title variants
Languages of publication
EN
Abstracts
EN
The world is now witnessing a tectonic shift in the way in which people react to social and economic impacts such as rise in fossil fuel prices, implication of new rules and regulations, and other situations which directly affect the emotions of a certain group of people. Violence is the most widely used way of expressing anger and discontent for a particular situation which might have occurred. Such actions can cause loss of millions of dollars and precious lives of people who come in way of such protests. These protests are mainly conducted through social media platforms such as twitter as it is not possible to personally communicate to tens of thousand people to accumulate at a certain place, therefore it is extremely important as well as necessary to keep an eye on the social media statuses and updates of people in the times of crisis and heavy tension. This paper aims to collect the tweets of people uploaded on twitter and then process them to find out the location, time and intensity of the mass violence so that the responsible authorities can handle the situation and prevent violence.
Year
Volume
113
Pages
227-234
Physical description
Contributors
author
  • Information Technology Department, Krishna Institute of Information and Technology, Ghaziabad - 201206, UP, India
author
  • Information Technology Department, Krishna Institute of Information and Technology, Ghaziabad - 201206, UP, India
References
  • [1] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Introduction to data mining. 2006 Pearson Addison-Wesley.
  • [2] C. Paper, ―Preprocessing Techniques for Text Mining Preprocessing Techniques for Text Mining. J. Emerg. Technol. Web Intell. no. October 2014.
  • [3] Wang, Yuan, et al. Hashtag graph based topic model for tweet mining. Data Mining (ICDM), 2014 IEEE International Conference on. IEEE.
  • [4] Christopher JC Burges. 1998. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery 2, 2 (1998), 121–167.
  • [5] Pak, Alexander, and Patrick Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. LREc. Vol. 10. No. 2010. 2010.
  • [6] Jiawei Han, Micheline Kamber, and Jian Pei. 2006. Data mining: concepts and techniques. Morgan Kaufmann.
  • [7] David A Hull et al. 1996. Stemming algorithms: A case study for detailed evaluation. JASIS 47, 1 (1996), 70–84.
  • [8] Hideki Isozaki and Hideto Kazawa. 2002. Efficient support vector classifiers for named entity recognition. In Proceedings of the 19th International Conference on Computational linguistics Volume 1. Association for Computational Linguistics,
  • [9] Mike James. 1985. Classification algorithms. Wiley - Interscience.
  • [10] Thorsten Joachims. 1998. Text categorization with support vector machines: Learning with many relevant features. Springer.
  • [11] Mehmed Kantardzic. 2011. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
  • [12] Leah S Larkey and W Bruce Croft. 1996. Combining classifiers in text catego-rization. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 289–297.
  • [13] Liddy, E.D. 2001. Natural Language Processing. In Encyclopedia of Library and Information Science, 2nd Ed. NY. Marcel Decker, Inc.
  • [14] Julie B Lovins. 1968. Development of a stemming algorithm. MIT Information Processing Group, Electronic Systems Laboratory.
  • [15] Catarina Silva and Bernardete Ribeiro. 2003. The importance of stop word removal on recall values in text categorization. In Neural Networks, 2003. Proceedings of the International Joint Conference on, Vol. 3. IEEE, 1661–1666.
  • [16] Ahmad, Sartaj & Varma, Rishabh. (2018). Information extraction from text messages using data mining techniques. Malaya Journal of Matematik. S. 26-29. 10.26637/MJM0S01/05.
  • [17] Jin, Lianjing, et al. A Text Classifier of English Movie Reviews Based on Information Gain. Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence (ACIT-CSI), 2015 3rd International Conference on. IEEE, 2015.
  • [18] Klahold, Andre, et al. Using word association to detect multitopic structures in text documents. IEEE Intelligent Systems 29.5 (2014): 40-46.
  • [19] Ansari, Fazel, Patrick Uhr, and Madjid Fathi. Textual meta-analysis of maintenance management’s knowledge assets. International Journal of Services, Economics and Management 6.1 (2014): 14-37.
  • [20] Wang, Hongning, Yue Lu, and Chengxiang Zhai. Latent aspect rating analysis on review text data: a rating regression approach. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACm, 2010.
  • [21] Ma, Zongyang, Aixin Sun, and Gao Cong. On predicting the popularity of newly emerging hashtags in twitter. Journal of the Association for Information Science and Technology 64.7 (2013): 1399-1410.
  • [22] Ramya, R. S., et al. Feature Extraction and Duplicate Detection for Text Mining: A Survey. Global Journal of Computer Science and Technology 16.5 (2017).
  • [23] Feldman, Ronen. Techniques and applications for sentiment analysis. Communications of the ACM 56.4 (2013): 82-89.
  • [24] Mohamad, Ismail Bin, and Dauda Usman. Standardization and its effects on K-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology 6.17 (2013): 3299-3303.
  • [25] Thompson, Dominic, and Ruth Filik. Sarcasm in written communication: Emoticons are efficient markers of intention. Journal of Computer‐Mediated Communication 21.2 (2016): 105-120.
  • [26] Jibril, Tanimu Ahmed, and Mardziah Hayati Abdullah. Relevance of emoticons in computer-mediated communication contexts: An overview. Asian Social Science 9.4 (2013): 201.
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-1ee6a822-6594-4912-ad63-988a757559d6
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.