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A semi-automated review classification system based on supervised machine learning

机译:基于监督机器学习的半自动评论分类系统

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The field of opinion mining is expanding rapidly with the widespread use of internet for e-commerce and social interaction. One of the interesting use of opinion mining is in the field of online producer-consumer industry. The primary goal of the work presented in this paper is to perform a semi-automated sentiment classification on online product reviews for product evaluation using machine learning. We also aim to induce simplicity in sentiment classification; by using a method called Dual Sentiment Analysis, we relegate the need of using complex human annotations or very high end linguistic tools to solve the polarity shift problem in opinion classification. We also propose use of a pseudo-opposites dictionary based on our training corpus which is domain consistent with the training dataset.
机译:随着互联网在电子商务和社交互动中的广泛使用,观点挖掘的领域正在迅速扩展。意见挖掘的有趣用途之一是在线生产者-消费者行业。本文提出的工作的主要目标是对在线产品评论执行半自动的情感分类,以使用机器学习进行产品评估。我们的目的还在于在情感分类中引入简单性。通过使用一种称为双重情感分析的方法,我们满足了使用复杂的人类注释或非常高端的语言工具来解决意见分类中的极性转移问题的需求。我们还建议根据我们的训练语料库使用伪对立字典,该字典与训练数据集的域一致。

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