Definition
Text analytics is the task of rearranging unstructured text documents into usable, structured data. Text analysis process involves splitting sentences and phrases into their components, and then studying each part’s role and meaning using complex software rules and machine learning algorithms. All these components, such as parts of speech, tokens, and chunks, play a crucial role in achieving deeper natural language processing and contextual analysis [1].
For instance, let’s take a look at the following example to illustrate text analytics: Let's say a company receives customer reviews for its products online, which can be a goldmine of information. But practically, it is not an easy task for humans to read and analyze thousands of reviews manually. This is where text analytics becomes useful. Text analytics system can automatically go over the reviews, seeking for patterns and sentiments. And by so doing, it can discover common words or phrases that customers use to express satisfaction or dissatisfaction. For example, it might discover that words like love, great, and excellent often appear in positive reviews, while words like disappointed, issues, and poor may appear in negative reviews [2].
A Text Analytic process for improving customer service experience [3]
Origin
The origins of text analytics can be traced back to the era of World War II when intelligence agencies where in a mission to decode enemy communications and scan through massive amounts of intercepted messages. This led to early computational linguistics, where researchers explored ways to automate text analysis using rule-based systems. Warren Weaver, a key figure in this movement, saw a future where machine translation could automatically convert one language to another. His ideas sparked some of the earliest natural language processing (NLP) efforts, relying on syntax rules and statistical models to analyze text. However, early machine translation systems struggled with accuracy, highlighting the complexity of human language.
By the 1960s,
text analytics methods evolved to include rule-based parsing, part-of-speech
tagging, and statistical modeling. These developments formed the foundation for
modern NLP, making it possible for machines to process, categorize, and extract
meaning from text—an essential step toward today’s AI-driven text analytics.
Context and
Usage
Text analytics
can be used almost anywhere, from improving customer experiences to spotting
new business opportunities. It’s a versatile tool that can solve real-world
problems across industries such as healthcare, finance, marketing, legal, etc.
Why it Matters
Text analytics
is a key concept in modern data analysis and business intelligence. With 80% of
business information being unstructured, how can you process all that data
manually? Text analytics powered by natural language processing (NLP) and
Artificial Intelligence (AI) is the answer. Businesses are easily recognizing
its value in improving customer experiences, refining products, and making
data-driven decisions. What makes text analytics so valuable is its ability to
uncover patterns and trends that would otherwise go unnoticed.
Text analytics will only grow in importance. In fact, its market reflects a growing demand with its global market expected to grow to $14.68 billion in 2025, and grow further to $78.65 billion by 2030. That’s at a 39.9% CAGR! [4].
In Practice
A real-life case
study of a company practicing text analytics in AI can be seen in the case of Flickr,
one of the most popular and important platforms for sharing photographs and
also one of the first websites that implemented a text analytics system based
on folksonomy. The portal uses text analytics to organize information and make
it easier for users to find the images they are looking for. Flickr's text
analytics algorithm also has the ability to recognize trends and highlight the
most relevant hashtags of the moment.
On the platform,
users of the Flickr community upload their photos and describe the content of
their work through tags. In addition, other users can add more tags to the
photographs even if they are not the authors of the photograph and can even tag
the location of the place where the photograph was taken [5].
See Also
Syntax Analysis: Understanding sentence structure
Text Summarization: Condensing content automatically
Tokens: Individual units (words, subwords, characters) that text is divided into for processing
Triple (Semantic Triple): Subject-predicate-object expressions
References
- Lexalytics. (2024). Text Analytics.
- Geeksforgeeks. (2024). What is Text Analytics ?
- Surveypal. (n.d). The Benefits of Text Analysis for Support Teams.
- Zapanta, K. (2025). What is Text Analytics? Your Beginner’s Guide to Transforming Data into Insight.
- Bismart. (2025). What Is Text Analytics? Find Out Through 5 Real Examples.