基于方面的意见挖掘方法的客户评论分析框架

S. Dasgupta, Jaydip Sen
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引用次数: 2

摘要

意见挖掘是处理人们的意见、评价、态度和情感及其不同方面的计算分支。近年来,这一领域引起了广泛的研究兴趣。在实际应用中经常需要方面级(称为基于方面的意见挖掘),因为它提供了关于实体和实体本身不同方面的详细意见或看法,这些通常是行动所必需的。因此,方面抽取和实体抽取是基于方面的意见挖掘的两个核心任务。他的论文提出了一个基于迁移学习概念的基于方面的意见挖掘框架。亚马逊网站上的真实客户评论。该模型在基于方面的意见挖掘任务中取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach
Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. his paper has presented a framework of aspect-based opinion mining based on the concept of transfer learning. on real-world customer reviews available on the Amazon website. The model has yielded quite satisfactory results in its task of aspect-based opinion mining.
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