Sentiment Analysis Using Konstanz Information Miner in Social Networks

Abstract

The sentiment analysis process that gives this work its name is the main theme of the work. Since the beginning of 2000, sentiment analysis has become one of the most active research areas by researchers working on natural language processing and social networking analysis. In addition, data mining, web mining, and text mining are also studied extensively. Moreover, the method of sentiment analysis has spread over many fields, from computer science to management science, from social science to economics, due to the importance given to the business world as a whole and the collectivity. In this study, Konstanz Information Miner (KNIME), which is a powerful data mining tool with its richest features and many visualization tools, was used on twitter data. Ten thousand Twitter data were used in this study. The sentiment analysis study, which is in fact a classification study, was conducted using machine learning algorithms on Twitter data. The results of the study were interpreted by carrying out an accuracy analysis. It is anticipated that the use of the KNIME, which has rich visualization tools, will be widespread in sentiment analysis studies to make these works both easier and more reliable.