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Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

机译:使用关联规则挖掘,模糊逻辑和本体论的基于上下文的评论分类

摘要

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
机译:由于易于在线共享客户体验,因此互联网促进了推荐系统的发展。总结和简化在线文本评论是一项艰巨的任务。在本文中,我们提出了一个新的框架,称为基于模糊的上下文推荐系统。为了对客户评论进行分类,我们基于用户给出的上下文从评论中提取信息。我们使用文本挖掘技术来标记评论并提取上下文。然后我们从本体数据库中找出上下文之间的关系。我们结合了基于模糊的语义分析器,以在未在评论和上下文之间找到它们之间的关系。基于句子的分类可预测相关评论,而基于模糊的上下文方法可预测相关评论中的相关实例。结合关联规则和本体挖掘进行文本分析。使用基于模糊规则的语义分析器比较评论及其上下文之间的关系。

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