意见挖掘类不平衡数据的应用综述

P. Babu, B. Battula
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引用次数: 0

摘要

观点挖掘或情感分析是从大量的文本信息或评论中分析关于产品或主题的有用信息。在二元产品评论(正面或负面)中,类的分布总是倾向于任何一个类,从而在数据集中产生类不平衡的性质。数据集的类不平衡状态是指一个类中的实例数量明显超过另一个类中的实例数量。现有的意见挖掘方法在类不平衡意见挖掘数据集上效率不高。本文介绍了类不平衡意见挖掘数据集的最新概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on applications of opinion mining class imbalance data
Opinion mining or sentiment analysis is to analyze the useful information from the large quantity of text messages or reviews regarding a product or a topic. In binary product reviews (positive or negative) the distribution of classes will always tend to any one class, thereby generating a class imbalance nature in the dataset. A class imbalance state of the dataset is in which, instances in one class predominately outnumber the instances in other class. The existing opinion mining approaches are not efficient on the class imbalance opinions mining datasets. In this paper, we present the up to date survey of class imbalance opinion mining datasets.
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