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Deep analysis of knowledge in one's writings

机译:对个人著作中的知识的深入分析

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摘要

Reading the opinion behind the text is a big challenge. In another way, we need to automatically read opinions and moods as a natural language. Ontology -based plays a main role to solve the problems in this field. That is from the features of the ontology based as covering the semantics of the concepts. So, in this paper, we propose a flexible classification opinion mining tool. This proposed method based on ontology- based. The proposed method uses NLTK (Natural Language Processing Toolkit) with Python as a useful knowledge to get more representative word occurrences in the corpus. Also, we not only use a WordNet and SentiWordNet ontologies to assign the word as POS (part of speech), but we also create a specific purpose ontology by OWL editor as Protégé. Then we create a more general opinion mining tool where the specific purpose ontology file was selected to use for classification the text. We apply our proposed method on lists of long texts for different writers, and then we can classify these writers depending on their writings.
机译:阅读文本背后的观点是一个巨大的挑战。换句话说,我们需要自动阅读意见和情绪作为自然语言。基于本体的技术在解决该领域的问题中起着主要作用。这是基于本体的特征,即覆盖了概念的语义。因此,本文提出了一种灵活的分类意见挖掘工具。本文提出的基于本体的方法为基础。所提出的方法使用带有Python的NLTK(自然语言处理工具包)作为有用的知识来在语料库中获得更具代表性的单词出现。同样,我们不仅使用WordNet和SentiWordNet本体将单词分配为POS(词性),而且还通过OWL编辑器Protégé创建了特定目的的本体。然后,我们创建一个更通用的意见挖掘工具,在其中选择了特定目的的本体文件来对文本进行分类。我们将建议的方法应用在针对不同作者的长文本列表上,然后可以根据他们的著作对这些作者进行分类。

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