May 1, 2024
Updated May 9, 2025
25 minute read
Text mining, also known as text data mining or text analytics, is the process of deriving high-quality information from text. It involves the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources such as websites, books, emails, reviews, and articles. Imagine teaching a computer to read and understand vast quantities of text, much like a human, but at a significantly faster pace and larger scale. This ability to transform unstructured text into a structured format allows for the identification of meaningful patterns, trends, and new insights. Approximately 80% of the world's data exists in an unstructured format, making text mining an incredibly valuable practice for organizations.
1195ge|
Find a path to becoming a Text Mining. Learn more at:
OpenCourser.com/topic/1195ge/text
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Text Mining.
Provides a theoretical foundation for statistical NLP techniques. It covers a wide range of topics, including language modeling, parsing, and machine translation.
Provides a practical introduction to text mining using the R programming language. It covers a wide range of techniques, including text cleaning, tokenization, stemming, and machine learning.
Provides a comprehensive overview of data mining techniques, including text mining. It covers a wide range of topics, including data preprocessing, clustering, classification, and association rule mining.
Provides a comprehensive overview of information retrieval techniques, including text mining. It covers a wide range of topics, including search engine design, web mining, and text classification.
Provides a comprehensive overview of statistical learning techniques, including text mining. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
Provides a comprehensive overview of machine learning techniques, including text mining. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of deep learning techniques, including text mining. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and transformers.
Provides a practical introduction to natural language processing (NLP) using the Python programming language. It covers a wide range of topics, including text classification, clustering, topic modeling, and sentiment analysis.
Provides a comprehensive overview of the SAS Text Miner software. It covers a wide range of topics, including data preparation, text mining algorithms, and reporting results.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/1195ge/text
Save
