ABSTRACT The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyze the social data at two levels: the entity level and the domain level. We have chosen Twitter as a social channel for the purpose of concept proof. Ontologies are used to capture domain knowledge and to enrich the semantics of tweets, by providing specific conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.

中文翻译：

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基于本体的社会大数据可信域识别方法

摘要 从社交媒体数据源管理和提取有用知识的挑战引起了学术界和工业界的广泛关注。为了应对这一挑战，本文重点关注文本数据的语义分析。我们提出了一种基于本体的方法来提取文本数据的语义并定义数据域。换句话说，我们在两个层面对社交数据进行语义分析：实体层面和领域层面。出于概念证明的目的，我们选择了 Twitter 作为社交渠道。通过提供出现在推文中的实体的特定概念表示，本体用于捕获领域知识并丰富推文的语义。案例研究用于证明这种方法。我们使用从 Twitter 和政治领域收集的公共数据集来试验和评估我们提出的方法。基于本体的方法在概念识别的数量和准确性方面利用实体提取和概念映射。