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Classification of Unwanted Messages in Online Social Network Using Machine Learning Algorithms

机译:使用机器学习算法对在线社交网络中不需要的消息进行分类

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This One major fact in today's technical world, people are very active users of Online Social Networks. They share every details of their day to day life and are in touch with their loved ones no matter in which part of the world they live. The main issue is the ability to control the messages that are posted in the user's private message or walls to detect and negotiate unwanted messages. This work focus on predicting the emotions of a particular message or post in various OSN like twitter, blogs etc for emotion analysis so as to filter the messages which are inappropriate. This paper focuses on collecting corpus for sentimental analysis and performs linguistic analysis and machine learning techniques for predicting emotions accurately. Using the corpus we define distinct emotions and filter unwanted messages.
机译:在当今技术世界中,这是一个重要事实,人们是在线社交网络的非常活跃的用户。他们分享日常生活中的每一个细节,无论他们生活在世界的哪个地方,都与亲人保持联系。主要问题是能够控制张贴在用户私人消息或墙上的消息以检测和协商不需要的消息的能力。这项工作的重点是预测特定消息或各种OSN(如Twitter,博客等)中的情绪,以进行情绪分析,以过滤不适当的消息。本文着重于收集语料库以进行情感分析,并进行语言分析和机器学习技术以准确地预测情绪。使用语料库,我们定义了不同的情感并过滤了不需要的消息。

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