An application on text mining with the R program

Data mining aims to analyze data in a statistically significant manner, particularly in datasets featuring numerical attributes or those represented in a numerical format. On the other hand, text mining involves the structured and systematic extraction of previously undiscovered and potentially valuable information from electronic text and document collections, such as textual files in formats like word or pdf, social media posts, and blog entries. Text mining seeks to derive meaningful results from text through statistical analysis. To extract information from textual data, certain processes must first be executed, and non-structurally formatted textual data needs to be transformed into a structured form. Subsequently, the text data transformed into a structured format becomes amenable to the application of data mining methods. Commencing with the collection of texts, this process culminates in meaningful information through the utilization of statistical and data mining methods after subjecting the collected texts to some data preprocessing and transformation procedures. In the thesis study, data mining and text mining applications and methods are discussed, and data obtained from YÖK Academy and Dergipark Application are subjected to initial analyses using R programming with data and text mining methods. Following the necessary analyses, word clouds were generated.