利用 MapReduce 范式,基于评级机制对比电影评论中的异常帖子

P. Gupta, Atul Sharma, J. Grover
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引用次数: 6

摘要

BigData 包含大量非结构化数据,如电影数据、Facebook 数据和行业数据等。不同用户在 Twitter 上发布了大量关于电影的帖子。在这些帖子中,有些可能是不恰当的。这些帖子中既有对电影的负面评论,也有正面评论。很难区分大量的正面和负面帖子。为了解决这类问题,我们提出了一种基于评分的机制,借助用户评分来区分异常帖子。如果评分是正面的,那么帖子就是正常的,否则就是异常的。为了实现所提出的机制,我们使用了 hadoop 平台和 MapReduce 范式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rating based mechanism to contrast abnormal posts on movies reviews using MapReduce paradigm
BigData contains large amount of unstructured data in the form of movie data, facebook data, and industry data and so on. There are number of posts are posted on Twitter about movies by different users. Out of these posts some of posts may be inappropriate. These posts contain negative comments as well as positive comments about movies. It is difficult to distinguish large number of positive and negative posts. To overcome this kind of problem we proposed a rating based mechanism that distinguishes abnormal posts with the help of users rating. If rating is positive then post is normal otherwise it is abnormal. To implement proposed mechanism we used hadoop platform and MapReduce paradigm.
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