Monitoring Suspicious Discussions on Social Media Case Study

TRIDIB CHAKRABORTY, Jeevan Krishna Ghosh, Puja Datta, Aliva Ghosh


The goal of data analysis is to selecting words from the social media like “face book”, “twitter”, “orkut” etc and then the words are analyzed using some algorithm like c4.5 or with any other algorithm, then the patterns are judged to discover that whether the word or comment can create any negative impact or not. If there is any chance of creating negative impact by that comment, it will be deleted. And it is the nature of some people making suspicious discussions and sometimes they cross their limits and this may create havoc in the society. So monitoring these staffs very carefully & effectively is necessary. The steps involve collecting data from social media known as Data Mining, analyzing them with some negative & positive words which are affecting peoples is known as Data Analysis, making decision with some effective Algorithms, & lastly Monitoring them carefully. It’s become much more helpful now-a-days to watch criminal activities and to deal with cyber crime.


Data Mining, Data Analysis, Algorithms, Monitoring.

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S.Somasundaran and J. Wiebe. Recognizing stances in ideological online debates. In workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 116-124. ACM,2010

Mostafa Karambeikar and Ali A. Ghorbani, Sentiment Analysis Of Social Issues. In 2012 International Conference on Social Informatics, pages 215-221. IEEE 2012

Nitin Indurkhya, Fred J. Damerau , Handbook of Natural Language Processing, Second Edition, CRC Press, 2010.

Ronen Feldman, James Sanger,The Text Mining Handbook-Advance Approaches in Analyzing Unstructured Data, Cambridge University Press,2007.

Jiawei Han, Micheline Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publications, 2006. [5] Ronen Feldman, “Techniques and Application of Sentiment Analysis”, Communication of ACM, April 2013, vol. 56.No.4.

Ana C.E.S Lima and Leandro Castro, “Automatic Sentiment Analysis of Twitter Messages”, IEEE Fourth International Conference on Computational Aspect .of Social Networks (CASoN), p.52-57, 2012.

Keisuke Mizumoto, Hidekazu Yanagimoto and Michifumi Yoshioka, “Sentiment Analysis of Stock Market News with Semi-supervised Learning”, IEEE Computer Society,IEEE/ACIS 11th International Conference on Computer and Information Science, p.325-328,2012.

Sang-Hyun Cho and Hang-Bong Kang, “Text Sentiment Classification for SNS-based Marketing Using Domain Sentiment Dictionary”, IEEE International Conference on Conference on consumer Electronics (ICCE), p.717-718, 2012.

Aurangzeb Khan and Baharum Baharudin, “Sentiment Classification Using Sentence-level Semantic Orientation of Opinion Terms form Blogs”, 2011.

Ms.K.Mouthami, Ms.K.Nirmala Devi, Dr.V.Murali Bhaskaran, “Sentiment Analysis and Classification Based on Textual Review”



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