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A context-based model for disambiguating the sentiment concepts using the common-sense knowledge
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Abstract: (2912 Views) |
Today, development of social networks and its popularity resulted in the creation of large amounts of data and content of their users. Analyzing the content produced by users can be very effective in achieving economic, political, cultural, social, and defense goals. Nowadays, many aspects of human life are affected by the influence of social networks on the Internet. Social networks have given rise to numerous threats and problems against their users, in addition to creating opportunities that have not existed before. Thus, in security reasons, sentiment analysis or opinion mining in social networks is significant.
In this paper, we proposed a novel sentiment analysis model based on the bag-of-concepts approach as well as commonsense knowledge. First, a method was proposed for understanding ambiguous sentiment concepts, and then a method was provided to disambiguate the polarities with the help of bag-of-concepts and its links with other concepts. The polarity of ambiguous concepts was more accurately detected when we took into account the positive and negative context vectors and the relations to the concepts in the Semantic Knowledge Base. Finally, the proposed model was evaluated by applying it on the review corpus, and the experimental results showed the effectiveness of the proposed model.
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| Keywords: Sentiment analysis, sentiment concept, disambiguation, Contextualization, Commonsense knowledge, bag-of-concepts |
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Full-Text [PDF 1021 kb]
(1314 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2018/06/20 | Accepted: 2018/10/16 | Published: 2019/04/28
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