Metin Madencili?i ile Bitcoin Hareket Yönü Tahmini

26th IEEE Signal Processing and Communications Applications Conference (2018 : Izmir; Turkey)In the last few years, Bitcoin is one of the most discussed and popular topic in financial system. This article aims to predict Bitcoin movement by using Machine Learning and Text Mining models. Many models have been used to this end, including the most popular models in financial prediction; Artifical Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). In addition to this, in order to examine the effect of daily news on Bitcoin movement, the text mining models are involved into the prediction system. This paper focuses on applying Machine Learning models on a integrated dataset, which contains both historical Bitcoin values and features from daily news text. Overall, our model can estimate the direction of Bitcoin with a high success. © 2018 IEEE.Bitcoin, yıllardır üzerinde durulan ve popülerligi gittikçe artan bir yatırım aracıdır. Bu çalışmada, yapay zeka ve metin madenciligi yöntemlerinin birleştirilmesiyle Bitcoin hareket yönünün tahmin edilmesi amaçlanmıştır. Bu dogrultuda, borsa tahmininde en çok kullanılan modeller olan Yapay Sinir Agı (YSA), Destek Vektör Makinesi (SVM) ve Lojistik Regresyon (LR) gibi makine ögrenme algoritmaları, Bitcoin hareketi kestiri- minde kullanılmıştır. Bunun yanında, günlük haberlerin Bitcoine olan etkisi ve bu etkinin makine ögrenme teknikleri ile birlikte kullanıldıgı durumlarda Bitcoin hareket yönü tahminine olan etkisini incelemek ve analiz etmek amacıyla, günümüzde sıklıkla kullanılan metin madenciligi tekniklerinden yararlanılmıştır. Çalışmada, Bitcoin tarihi verilerini içeren veri seti ile metin madenciligi teknikleri kullanılarak, günlük haber başlıklarından oluşturulan veri seti birleştirilerek elde edilen tek bir veri seti üzerinde makine ögreme algoritmalarının çalıştırılması önerilmiştir. Bu şekilde, Bitcoin hareket yönünün tahmininde yüksek başarı elde edilebilecegi de gerlendirilmiştir.Aselsan,et al.,Huawei,IEEE Signal Processing Society,IEEE Turkey Section,Neta

From social media analysis to ubiquitous event monitoring: The case of Turkish tweets

9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017 : Sydney; Australia)The work described in this paper illustrates how social media is a valuable source of data which may be processed for informative knowledge discovery which may help in better decision making. We concentrate on Twitter as the source for the data to be processed. In particular, we extracted and captured tweets written in Turkish. We analyzed tweets online and real-time to determine most recent trending events, their location and time. The outcome may help predicting next hot events to be broadcasted in the news. It may also raise alert and warn people related to upcoming or ongoing disaster or an event which should be avoided, e.g., traffic jam, terror attacks, earthquake, flood, storm, fire, etc. To achieve this, a tweet may be labeled with more than one event. Named entity recognition combined with multinomial naive Bayes and stochastic gradient descent have been integrated in the process. The reported 95% success rate demonstrate the applicability and effectiveness of the proposed approach. © 2017 Association for Computing Machinery.ACM SIGMOD,Gemalto,IEEE Computer Society,IEEE TCDE,Springer Natur