A Framework for Classifying Indonesian News Curator in Twitter
| Autor: | Jaka Eka Sembodo, Z. K. A. Baizal, Erwin Budi Setiawan |
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| Rok vydání: | 2017 |
| Předmět: |
Class (computer programming)
Computer science Indonesian news curator Overfitting computer.software_genre language.human_language News aggregator World Wide Web Indonesian Naive Bayes classifier Machine learning language Feature (machine learning) Selection (linguistics) InformationSystems_MISCELLANEOUS naïve Bayes classifier Electrical and Electronic Engineering computer |
| Zdroj: | TELKOMNIKA (Telecommunication Computing Electronics and Control). 15:357 |
| ISSN: | 2302-9293 1693-6930 |
| DOI: | 10.12928/telkomnika.v15i1.4557 |
| Popis: | News curators in twitter are a user, which is interested in following, spreading, giving feedback of recent popular articles. There are two kinds of this user, news curator as human user and news aggregator as bot user. In prior works about news curator, the classification system built using followers, URL, mention and re-tweet feature. However, there are limited prior works for classifying Indonesian News Curator in twitter and still hard for labeling data involve just two features: followers and URL. In this paper, we proposed a framework for classifying Indonesian news curator in twitter using Naïve Bayes Classifier (NBC) and added features such as location, bio profile, and common tweet. Another purpose for analyzing the influential features of certain class, so it’s make easier for labeling data of this role in the future. Examination result using percentage split as evaluating system produced 87% accuracy. The most influential features for news curator are followers, bio profile, mention and re-tweet. For news aggregator class are followers, location, and URL. The rest just common tweet feature for not both class. We implemented Feature Subset Selection (FSS) for increasing system performance and avoiding the over fitting data, it has produced 92.90% accuracy. |
| Databáze: | OpenAIRE |
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