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dc.contributor.authorKilimci, Zeynep Hilal
dc.contributor.authorYörük, Hasan
dc.contributor.authorAkyokuş, Selim
dc.date.accessioned2021-01-11T11:25:56Z
dc.date.available2021-01-11T11:25:56Z
dc.date.issued2020en_US
dc.identifier.citationKilimci, Z. H., Yörük, H. ve Akyokuş, S. (2020). Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms. 2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTA. Novi Sad, Serbia, 24-26 August 2020. https://dx.doi.org/10.1109/INISTA49547.2020.9194624en_US
dc.identifier.isbn9781728167992
dc.identifier.urihttps://dx.doi.org/10.1109/INISTA49547.2020.9194624
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6221
dc.description.abstractCustomer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In the gaming market, it is observed that the demand on game applications rises with the usage of mobile devices such as smartphones. Because of this, it is important to predict when players tend to leave a game. Studies so far focus on churn prediction in mobile or online games by analyzing demographic, economic, and behavioral data about their customers. In this work, we introduce a sentiment analysis-based churn prediction model in mobile games using word embedding models and deep learning algorithms. To the best of our knowledge, this is the first study to evaluate the churn tendency of customers by analyzing sentiments of players from their comments on games using deep learning and word embedding models. For this purpose, we use deep learning algorithms for classification and word embedding models for text representation. The applied deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks. Word2Vec, GloVe, and FastText word embedding models are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are carried out on Turkish four different game datasets. The experiment results show that sentiment analysis of players in mobile games can be powerful indicator in terms of predicting customer churn.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectChurn Predictionen_US
dc.subjectDeep Learningen_US
dc.subjectMobile Gamesen_US
dc.subjectSentiment Analysisen_US
dc.subjectWord Embeddingsen_US
dc.titleSentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithmsen_US
dc.typeconferenceObjecten_US
dc.relation.ispartof2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTAen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authorid0000-0003-0793-1601en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/INISTA49547.2020.9194624en_US


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