What is text data mining?
Text data mining involves searching a text document or resource to obtain valuable structured information. This requires sophisticated analytical tools that process text to obtain specific keywords or key data points from relatively unformatted or unstructured formats.
Text data mining is also known as text mining or text analysis.
In text data mining, engineered systems use taxonomies and lexical analyzes to determine which parts of a text document are valuable as mined data. Statistical models are usually useful, and systems can also use heuristics or algorithmic guessing to try to determine which parts of a text are important.
Other control systems include tagging and keyword analysis, where tools look for specific proper names or other tags and keywords to find out what is being written about.
Another unique component of text mining is often referred to as sentiment analysis. In sentiment analysis, which is generally much more difficult than statistical analysis, analytical tools attempt to find out the mood or feeling behind the written text and other aspects of what it addresses on a very subjective and intuitive level.
With the advent of artificial intelligence tools, a lot of progress has been made in sentiment analysis, so modern text data mining is more than just collecting quantitative references and incorporating high-level design models into text mining to break new ground find to collect valuable data.