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Exploiting Feature Selection Algorithm on Group Events Data Based on News Data

机译:基于新闻数据的团体事件数据特征选择算法开发

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As traditional media and new media such as Weibo and WeChat are increasingly used, Internet has become the main supporter of thoughts of groups or individuals, which plays a key role in guidance of our daily life and society development. This study aims to investigate/propose a note feature extraction algorithm of data processing in massive news data, extracting the key words in the news and clarifying the important ones. We try to propose a revised STF algorithm and have comparisons between efficiency of various algorithms. Experiments showed that the proposed algorithm is 4% higher on the classification accuracy than other algorithms.
机译:随着传统媒体和微博,微信等新媒体的使用,互联网已成为群体或个人思想的主要支持者,在指导我们的日常生活和社会发展中发挥着关键作用。这项研究旨在研究/提出海量新闻数据中数据处理的音符特征提取算法,提取新闻中的关键词并弄清重要的关键词。我们尝试提出一种改进的STF算法,并比较各种算法的效率。实验表明,该算法的分类精度比其他算法高4%。

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