21 Text Mining
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Text Mining (or Text Analytics) applies analytic tools to analyses and to learn from collections of text data. Text data might include social media posts, books, newspapers, emails, research papers, etc. The goal can be similar to humans learning by reading such material. Using automated algorithms we can learn from massive amounts of text, very much more than any human can, and indeed with the advent of large language models the power of this learning is evident. Such large language models have collected together all of the text from the Internet, converting videos, for example, to text as well, leading to an unimaginable amount of text data.
With any corpus of text material (e.g., today’s newspapers) we might begin by summarising the main themes and to identify those that are of most interest to us. Or we might be monitoring social media feeds to identify emerging topics that we might need to act upon, as they emerge.
You can download a small selection of txt files to form your
corpus for exploration. From Togaware download
corpus_papers.zip. Once
downloaded unzip the archive into corpus/txt/. We will use this
corpus to illustrate our text mining capabilities.
If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
