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    Anomaly Detection in Bitcoin Prices using DBSCAN Algorithm
    (2020) Dokuz, Ahmet; Çelik, Mete; Ecemiş, Alper
    Blockchain is an emerging technology which is also behind the Bitcoin digital money. Daily bitcoin transactions are increasing due tothe popular and widespread investments. The increase of Bitcoin related datasets and this increased big dataset requires novelapproaches and methods to analyze using data mining techniques. In addition, fluctuations and anomalies in the bitcoin prices couldmean a great deal to economists and discovering anomalies in bitcoin prices is important. In this study, anomaly detection in Bitcoinprices is performed based on the change of Bitcoin price difference and the change of Bitcoin price difference in percentage withrespect to previous day using 8-years of Bitcoin price dataset of the period of 2012-2019. First, the dataset is pre-processed andunnecessary columns are deleted. Then, 2 different datasets are created by using daily bitcoin prices, i.e. bitcoin price differencedataset and bitcoin price difference in percentage dataset. After that, for detecting anomalous price changes, DBSCAN algorithm andstatistical method are used, and the performance of the algorithms are evaluated. The results show that the DBSCAN algorithm andstatistical method successfully detects anomalies in bitcoin prices for both of the datasets. However, the DBSCAN algorithm performsbetter than the statistical method which could detect anomalies even they are close to the normal daily price changes. Also, in thisstudy, bitcoin price difference dataset and bitcoin price difference in percentage dataset are compared and the differences of theresults for both datasets and their reasons are explained.
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    II. International Turkic World Congress on Science and Engineering: Book of Abstracts and Book of Proceedings, 14-15 Kasım 2020
    (Niğde Ömer Halisdemir Üniversitesi, 2020) Ecemiş, Alper; Merzadinova, Gulnara; Barut, Murat; Tetir, Recep
    [Abstract Not Available]
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    Temporal Sentiment Analysis of Socially Important Locations of Social Media Users
    (Springer Science and Business Media Deutschland GmbH, 2021) Ecemiş, Alper; Dokuz, Ahmet Şakir; Celik, Mete
    Socially important locations are the places which are frequently visited by social media users. Temporal sentiment analysis of socially important locations is the process of interpretation and classification of emotions within their sharings in their socially important locations over time. Observing the temporal sentiment changes in these locations helps both to examine the emotion change in the locations and to understand the thoughts of the social media users in these locations. In this paper, Twitter is selected as social media data source and temporal sentiment analysis of socially important locations of social media users are analyzed in different time frames. For the analysis, a method, called Temporal Sentiment Analysis of Socially Important Locations (TS-SIL), is proposed in this study. In this method, first of all, socially important locations are discovered from the collected Twitter dataset. Then, sentiment analysis is performed using a dictionary based approach and several machine learning algorithms. Finally, the sharings in the locations are listed and the sentiments at these locations are analyzed by daily, weekly, and monthly basis. As a result, socially important locations of the city of Istanbul are discovered and temporal sentiment analysis of these locations are performed. Results shows that all of the socially important locations of İstanbul, except Beşiktaş Fish Market, showed emotional fluctuations over the time. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.