Text analytics

Text analytics, or text mining, uses computational methods to extract useful structure, patterns and signals from collections of unstructured language.

When to use it

Five common tasks define much of the field:

Text categorisation

assigns documents to known classes, such as topic, document type or author. It supports spam detection, email routing and searchable archives.

Text clustering

discovers groups without requiring predefined labels. A search for ‘cell’, for example, may separate results about biology, batteries and prisons.

Concept extraction

identifies entities, topics and relationships. In legal discovery, it can narrow millions of documents to the material most likely to matter.

Sentiment analysis

, also called opinion mining, estimates whether language expresses positive, negative or neutral attitudes and helps reveal patterns beyond literal wording.

Document summarisation

produces a shorter representation of the important content, helping readers triage large volumes of material.

Together, these methods support retrieval, tagging, pattern recognition, information extraction and predictive work. Useful business questions include:

• What do customers or employees think about a product? See Sentiment Analysis.
• How is the employment brand perceived in public conversation?
• Which complaints recur most often?
• What themes are emerging in on-site search behaviour?

Origins

Text analytics has no single inventor. It developed from information retrieval, computational linguistics, statistics and natural-language processing. Early systems focused on indexing and word frequency; later machine-learning methods made classification, clustering and sentiment analysis practical at scale. Contemporary systems combine linguistic rules, statistical models and, increasingly, learned language representations.

What it is

Businesses hold large text collections in documents, email, reports, customer records, websites, blogs and social channels. Humans can read those materials, but conventional databases cannot analyse prose as neatly as rows and columns. Traditional metadata—file name, author and creation date—helps retrieve a known document, and keyword search finds a known term. Neither reliably discovers an unanticipated pattern.

Text analytics converts language into analysable features so a system can surface change, association and anomaly. It might detect a decline in customer sentiment, reveal an emerging product request or connect issues that were previously scattered across thousands of records. The output is evidence for interpretation, not an automatic statement of truth: irony, domain language, multilingual text and biased training data can all affect results.