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Building Ontology for Different Emotional Contexts and Multilingual Environment in Opinion Mining

Wan Taoa,b, Tao Liua,b

a School of Computer and Information Science, Anhui Polytechnic University, Wuhu, China;
b Key Laboratory of Computer Application Technology, Anhui Polytechnic University, Wuhu, China

* Corresponding Author: Wan Tao, email

Intelligent Automation & Soft Computing 2018, 24(1), 65-72.


With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ontology DEMLOnto would help in identifying the opinion features associated with the contextual environment, which may change along with applications. We built the ontology according to ontology engineering. It was developed on the platform Protégé by using OWL2.


Cite This Article

W. Tao and T. Liu, "Building ontology for different emotional contexts and multilingual environment in opinion mining," Intelligent Automation & Soft Computing, vol. 24, no.1, pp. 65–72, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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