Text mining, also known as text analytics or natural language processing (NLP), is a field of study and practice that involves extracting meaningful information and knowledge from unstructured textual data. The goal of text mining is to convert large volumes of text into structured data that can be analyzed to discover patterns, relationships, and insights. Silge and Robinson expertly cover these areas, and although the book is relatively thin (less then 200 pages, including the table of contents and index), it covers most areas of text mining to get new users up and running quickly.

The key aspects and processes involved in text mining, most of which are touched on in the book, include: text preprocessing (including tokenization, normalization, and stopword removal); text analysis techniques (including sentiment analysis, named entity recognition (NER), identifying and classifying entities, topic modeling, and text classification); feature extraction (bag of words (BoW), term frequency-inverse document frequency (TF-IDF)); machine learning and statistical methods (clustering, classification, regression analysis); and text visualization (word clouds, topic visualization, sentiment visualization).

There are as many applications of text mining as there are “comments” boxes or “tell us more” questions on a form or survey. Examples of applications of text mining include:

• Information retrieval: enhancing search engines to retrieve relevant documents based on user queries.
• Customer feedback analysis: analyzing customer reviews and feedback to understand opinions and improve products/services.
• Healthcare: extracting information from medical records, clinical notes, and research literature.
• Financial analysis: analyzing financial reports, news articles, and social media for market trends and sentiments.

Text mining plays a crucial role in making sense of the vast amount of unstructured textual data available on the Internet and in various industries, enabling organizations to make informed decisions and gain valuable insights.

This book is ideal for experienced R users who are looking to expand their knowledge base in order to start delving into text analytics.

More reviews about this item: Amazon