Abstract
In recent years, companies have begun using social networks as a useful tool for marketing campaigns and communicating with their customers. Social networks are enormously beneficial for both sellers and buyers. For producers, the analysis of social content can provide immediate insight into people’s reactions regarding products and services. Meanwhile, from such content, customers can know about products and services, and whether they are good or not. More importantly, users want to know about the degree of risk related to such products or services, which is a practical issue that needs to be resolved. Numerous studies have attempted to address this problem using a variety of methods. However, previous approaches have frequently predicted risk based on the user’s satisfaction regarding a specific entity (product, service, etc.). Therefore, some information, such as the user’s dissatisfaction and hesitation regarding an entity, has not yet been considered. In addition, there are very few methods for pointing out the aspects of an entity with a high degree of risk. These factors lead to low accuracy in terms of risk prediction. In this study, we introduce a method for detecting the degree of risk in an online market when considering not only the user’s satisfaction but also their dissatisfaction and hesitation regarding the given entity based on tweets sentiment analysis. The results prove the efficacy of the proposed approach in terms of the \(F_1\) score and received information.
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Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D. (2020). Detecting the Degree of Risk in Online Market Based on Satisfaction of Twitter Users. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_5
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