dc.contributor.author | Emre Isik Y. | |
dc.contributor.author | Görmez Y. | |
dc.contributor.author | Kaynar O. | |
dc.contributor.author | Aydin Z. | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T09:32:54Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T09:32:54Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9781538668788 | |
dc.identifier.uri | https://dx.doi.org/10.1109/IDAP.2018.8620913 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/5649 | |
dc.description | 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- | en_US |
dc.description.abstract | Today, people often share their ideas, opinions and feelings through forums, social media sites, blogs and similar platforms. For this reason, access to these data has become very easy. Increase in the number of shares makes it possible to analyze and use these data in terms of marketing and politics. However, due to the large number of data, it is impossible that this analysis will be done by humans. Determination of what type of emotion is included automatically is done by sentiment analysis methods. In these methods, the text is defined as a mathematical vector and classified by machine learning methods. Ensemble methods are one of the most important methods used as classifiers in sentiment analysis. In these methods, a classifier error is tried to be solved by another classifier. In sentiment analysis, the feature vector that describes the text is as important as the classifier. Feature vectors obtained using different methods can make mistakes in different places. For this reason, in this study, NSEM is proposed for sentiment analysis, which is a new ensemble method that uses 2 different classifiers and 2 different feature extraction methods. As a result of the analysis, the proposed method is the most successful method with an accuracy rate of 79.1%. © 2018 IEEE. | en_US |
dc.language.iso | tur | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.isversionof | 10.1109/IDAP.2018.8620913 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | ensemble method | en_US |
dc.subject | machine learning | en_US |
dc.subject | sentiment analysis | en_US |
dc.subject | stacked ensemble methods | en_US |
dc.title | NSEM: Novel Stacked Ensemble Method for Sentiment Analysis [NSEM: Duygu Analizi için özgün Yi?inlanmiş Topluluk Yöntemi] | en_US |
dc.type | conferenceObject | en_US |
dc.relation.journal | 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 | en_US |
dc.contributor.department | Emre Isik, Y., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey -- Görmez, Y., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey -- Kaynar, O., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey -- Aydin, Z., Bilgisayar Mühendisli?i, Abdullah Gül Üniversitesi, Kayseri, Turkey | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |