用于意见挖掘的混合过滤

Archana Gupta, Ajita Verma, Parul Kalra
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引用次数: 0

摘要

社交媒体越来越多地参与到当前的商业世界中,这影响了在线客户对产品和服务的不平衡看法。本文的主要目的是规范大众对各种社交媒体社区中任何产品或服务的意见。对于特定产品或服务的大量意见是可以获得的,因为通过社交媒体可以公开表达他们的意见。有大量的顾客对各种产品和服务的评论,这些评论没有系统地安排。此外,没有适当的机制来识别可靠或真实的审查。这促使组织和研究人员创造工具,可以自动分析和系统地排列那些真实的意见,过滤掉虚假的评论。主要的重点是借助真值检验来构建一个过滤模型。每个来源都需要经过真相测试,如果来源是真实的,那么在做出决定时得出结论,否则就会被标记为虚假评论,从而从系统中过滤掉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid filtering for opinion mining
The growth in involvement of social media with current business world has influenced online customers by unbalanced opinions about the product and services. The main objective of this paper is to standardize the opinion given by the masses about any product or services in various social media communities. Enormous opinions on a specific product or service is available due to vast exposure available for publicly voice their opinion through social media. There are enormous customer reviews available about various products and services which are not systematically arranged. Moreover, there is no proper mechanism to identify the reliable or genuine reviews. This motivates the organizations and researchers to create tools which can automatically analyze and systematically arrange only those opinions that are genuine and filter out the fake reviews. The principal focus is to frame a filtering model with the help of truth test. Every source needs to undergo through a truth test, if the source is genuine, then the opinion is concluded while making a decision else it is marked as a fake review and hence filtered from the system.
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