• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.115-122
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• 2014

One of the problems with current youth generations is increasing rate of violence and suicide in their school lives, and this study aims at the design of a sentiment analysis system to prevent suicide by uising big data process. The main issues of the design are economical implementation, easy and fast processing for the users, so, the open source Hadoop system with MapReduce algorithm is used on the HDFS(Hadoop Distributed File System) for the experimentation. This study uses word count method to do the sentiment analysis with informal data on some sns communications concerning a kinds of violent words, in terms of text mining to avoid some expensive and complex statistical analysis methods.

• Convergence Security Journal
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• v.15 no.4
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• pp.113-119
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• 2015

Recent, cyber violence is increasing in a school and the severity of the problems encountered day by day. In particular, the severity of the cyber force using the smart phone is recognized as a very high and great problems socially. Cyberbullying have long damage degree and a wide range time duration against of existed physical cyber violence. Then student's affects is very seriously. Therefore, we analyzes the relationship and languages in the classroom for students to use to identify signs of cyber violence that may occur between friends in the class. And we support this information to identified parent, classroom teachers and school sheriff for prevent cyberbullying accidents in the school. For this research, we will design and implement a messenger in the cyber classroom. It have many components that are Big-data vocabulary, analyzer, and communication interface. Our proposed messenger can analyze lingual sign and friendship between students using Big-data analysis method such as text mining. It can analysis relationship by per-student, per-classroom.