Mapreduce ve makine öğrenmesi yöntemleri ile büyük sosyal veride duygu analizi ve fikir madenciliği

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics identifying and subjective information extracting from text documents. The main task of sentiment analysis is to determine the polarity of a given text. The aim of this research is to investigate sentiment analysis for big social data using MapReduce and machine learning with KNIME tool. The datasets used in this research are fetched from the Internet in a real-time basis. The model used in the research is employing the current date and time for receiving data and following an up-to-date schema. It gathers text fields as input vectors from the most frequently written phrases on Twitter. The collected datasets are passed to an evaluating stage that employs both confusion matrix and cross validation methods. Twitter API is another tool that was used to gather data from Twitter in this work. The collected data was fed to KNIME tool from Hadoop platform. Also, for data classification purpose, machine learning algorithms were used. Keywords: Hadoop, big data, KNIME tool, MapReduce, sentiment analysis, Twitter