Abstract
As more and more users express their opinions on many topics on Twitter, the sentiments contained in these opinions are becoming a valuable source of data for politicians, researchers, producers, and celebrities. These sentiments significantly affect the decision-making process for users when they assess policies, plan events, design products, etc. Therefore, users need a method that can aid them in making decisions based on the sentiments contained in tweets. Many studies have attempted to address this problem with a variety of methods. However, these methods have not mined the level of users’ satisfaction with objects related to specific topics, nor have they analyzed the level of users’ satisfaction with that topic as a whole. This paper proposes a decision-making support method to deal with the aforementioned limitations by combining object sentiment analysis with data mining on a binary decision tree. The results prove the efficacy of the proposed approach in terms of the error ratio and received information.
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Acknowledgment
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410). And this work has supported by the National Research Foundation of Korea (NRF) grant funded by the BK21PLUS Program (22A20130012009).
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Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D. (2019). Decision-Making Support Method Based on Sentiment Analysis of Objects and Binary Decision Tree Mining. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_64
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