"Text Mining and Visualization: Case Studies Using Open-Source Tools" is a book that covers text mining and visualization techniques using open-source tools. The book was written by Andrew Chisholm and Markus Hofmann, who are experienced data scientists and educators.
The book is divided into three parts:
Introduction to Text Mining: This section covers the basics of text mining, including text preprocessing, feature extraction, and topic modeling. It also covers the use of open-source tools for text mining, such as NLTK, Gensim, and scikit-learn.
Text Visualization: This section covers the visualization of text data using open-source tools, such as D3.js and Tableau. It also covers techniques for visualizing text data, such as word clouds, scatterplots, and network diagrams.
Case Studies: This section covers case studies of text mining and visualization in various domains, such as social media, news articles, and scientific publications. It provides practical examples and code snippets using open-source tools for text mining and visualization.
The book is suitable for data scientists, machine learning practitioners, and anyone interested in learning about text mining and visualization using open-source tools. It assumes some familiarity with Python programming and basic data analysis concepts.
"Text Mining and Visualization" provides a comprehensive overview of text mining and visualization techniques using open-source tools. It is a useful resource for anyone looking to gain insights from text data, whether for business or research purposes.
