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    Effectiveness of social media in stock market price prediction based on machine learning
    (Springer International Publishing Ag, 2022) Karaşahin, Emre; Utku, Semih; Öztürkmenoğlu, Okan
    Trying to predict the future using social media data and analytics is very popular today. With this motivation, we aimed to make stock market predictions by creating different analysis models for 10 different banks traded in Borsa Istanbul 100 over 3 different groups that we selected on social media. The groups determined within the scope of the study can be detailed as tweets posted by banks from their accounts, tweets posted with the name of the bank, and tweets with the name of the bank posted from approved accounts. In our analysis, we used various variations, including the tweets' sentiments, replies, retweet and like counts of the tweets, the effects of daily currency (Dollar, Euro, and Gold) prices, and the changes in stock changes up to 3 days. To apply some pre-processing techniques to the collected data, we defined sentiment classes for sentiment analysis, created 6 different models, and analyzed it using 7 different classification algorithms such as Multi-Layer Perceptron, Random Forest, and deep learning algorithm. After all the models and analysis, we got a total of 1440 different results. According to our results, the accuracy rates vary according to the data groups and models we have chosen. The tweet group in which the name of the banks is mentioned can be shown as the most successful data group and we can easily say that there is a certain relation between social media and stock market prices.
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    Information retrieval-based bug localization approach with adaptive attribute weighting
    (Tubitak Scientific & Technical Research Council Turkey, 2021) Erşahin, Mustafa; Utku, Semih; Kılınç, Deniz; Erşahin, Buket
    Software quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development standards. The aim of this study is to build an adaptive IRBL tool and make it usable by more companies. BugSTAiR solves the aforementioned problem by means of the adaptive attribute weighting (AAW) algorithm and is evaluated on four open-source projects which are well-known benchmark datasets on BL. One of them is BLIA which is the state of the art in bug localization area and another is BLUIR which is a well-known BL tool. According to the promising results of experiments, Top1 rank of BugSTAiR is 2% and MAP is 10% better than BLIA's results on AspectJ and it has localized 4.6% of all bugs in Top1 and its precision is 6.1% better than BLIA on SWT, respectively. On the other side, it is 20% better in the Top1 metric and 30% in precision than BLUIR.