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Text data mining involves combing through a text document or resource to get valuable structured information. This requires sophisticated analytical tools that process text in order to glean specific keywords or key data points from what are considered relatively raw or unstructured formats.
Text data mining is also known as text mining or text analytics.
In text data mining, engineered systems use things like taxonomies and lexical analysis to determine what parts of a text document are valuable as mined data. Statistical models are commonly useful, and systems may also use heuristics, or algorithmic guesswork, 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 nouns or other tags and keywords to figure out what is being written about.
Another unique component of text mining is often called sentiment analysis. In sentiment analysis, which is generally much more difficult than statistical analysis, analytical tools try to figure out the mood or sentiment behind the written text and other aspects of what it is addressing on a very subjective and intuitive level. With the emergence of artificial intelligence tools, a lot of advancement has been done in sentiment analysis, such that modern text data mining is more than just collecting quantitative references and involves bringing high-level conceptual models to text mining to figure out new and unique ways to aggregate valuable data.