Over the last two decades, the amount of existing data sources in the world have dramatically increased due largely to digitalization. In parallel, data analysis has become a crucial topic for researchers in many areas. One of the essential perspectives in data analysis is text mining. In various forms, textual data is the most generated data element compared to multimedia data. Since the available data sizes are exponentially increasing, we need intelligent computational methodologies to handle massive datasets. Data mining approaches, specifically text mining techniques, come into prominence. The application of both text mining and machine learning techniques together on data analysis provides decent solutions. For that purpose, this book is prepared with four major chapters discussing various aspects of data analysis with text mining methods, such as clustering, classification, sentiment analysis, and prediction tasks implemented in the Python programming language.

Text Mining Applications Using Real-World Data in Python

Text Mining Applications Using Real-World Data in Python

232.00 TL

Text Mining Applications Using Real-World Data in Python