Hakdagli, O.Özcan, C.Ogul, I.U.2024-09-292024-09-292018978-153861501-0https://doi.org/10.1109/SIU.2018.8404540https://hdl.handle.net/20.500.14619/9258Aselsan; et al.; Huawei; IEEE Signal Processing Society; IEEE Turkey Section; Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- Izmir -- 137780With today's developing technology, people's access to information and its production have reached a very fast level. These generated and obtained information are instantly created, entered into data systems and updated. Sources of streaming data can be transformed into valuable analysis results when they are handled with targeted methods. In this study, a text data field is determined to perform analysis on instantaneous generated data and Twitter, the richest platform for instant text data, is used. Twitter instantly generates a variety of data in large quantities and it presents it as open source using an API. A machine learning framework Apache Spark's stream analysis environment is used to analyze these resources. Situation analysis was performed using Support Vector Machine, Decision Trees and Logistic Regression algorithms presented under this environment. The results are presented in tables. © 2018 IEEE.trinfo:eu-repo/semantics/closedAccessApache SparkMachine LearningSpark StreamingText MiningTwitterConference Object10.1109/SIU.2018.84045402-s2.0-850508128084N/A1