利用大数据分析和风险建模减少Twitter的犯罪

Chirag Kansara, Rakhi Gupta, S.D Joshi, S. Patil
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引用次数: 9

摘要

本文着重于一种从twitter获取人们情绪的方法,并用它来分析他们是否会对特定的人或社会构成威胁。机器学习是一种用于决策的技术,也可以通过从数据集中学习更多来进行预测。由于大量的数据流非常快,需要在短时间内进行分类。在本文中,我们的目标是通过使用Naïve贝叶斯分类器的大数据来实现更快的分类。我们已经通过使用HIVE来存储和处理数据来证明这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crime mitigation at Twitter using Big Data analytics and risk modelling
This paper focuses on a method to obtain sentiments of people from twitter and using it to analyze whether they can become threat to particular person or society. Machine learning is one of the technologies which is used for decision making and also make predictions by learning more from dataset. Since huge amount of data is streaming very fast, it needs to be classified in short duration. In this paper we aim to achieve faster classification by using Big Data with Naïve Bayes Classifier. We have demonstrated this by using HIVE to store and process data.
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