Abstract
The rapid development of social media has made more and more users express their opinions, feelings, and attitudes toward various things through different forums like Twitter, WeChat, and Weibo. However, most existing works just focus on specific product categories to construct the domain ontology, which is a quite narrow use of domain ontology. We propose a new construction of domain ontology based on the semantic features of social media. The topic of posts and opinions, also known as topic-opinion pairs, are identified with the domain ontology. The sentiment polarities are determined with the help of the given sentiment polarities. The sentiment polarity of an unknown post is calculated by the weighted average of the sentiment polarities of topics and opinions contained in the post. Preliminary results show that the application of domain ontology can effectively identify the topic-opinion pairs, and according to the known polarity of posts can effectively classify the topic-opinion pairs. The accuracy of sentiment classification is increasing.
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Wang, D., Xu, L., Younas, A. (2018). Social Media Sentiment Analysis Based on Domain Ontology and Semantic Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_3
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DOI: https://doi.org/10.1007/978-3-319-96136-1_3
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