Text Categorization: What Is It and How Does It Work?

Niraj Sharma |
 11/17/21 |
6 min read

Text Categorization: What Is It and How Does It Work?

The world is awash in data. Unfortunately for brands, most of it is unstructured and flung into the vast reaches of the internet. There’s a wealth of consumer insight and market intelligence out there, and text categorization helps us capture it – and make sense of it all – using artificial intelligence (AI). Here, we’ll break down text categorization by digging further into the following:

  • What is text categorization?
  • Why is it important?
  • How text categorization gives brands a leg up

And we uncovered the following statistics that will lend context to the discussion

  • 80% of the world’s data is unstructured.
  • It’s estimated that by 2025, global data generated on a daily basis will reach 463 exabytes. To make that easier to comprehend, one exabyte is equal to 1.04 million terabytes.
  • Natural language processing (NLP) is the tech behind text categorization. The global revenue from the NLP market was $3 billion in 2017 and is predicted to increase 14-fold to $43 billion in 2025.

Potentially useful data is everywhere, and brands adept at capturing it have an extreme advantage over those that aren’t. Let’s look at what text categorization is and how it works.

importance of trend analytics

What is Text Categorization?

Fundamentally, text categorization is the classification of text-based datasets. This can be done on a document level, like classifying books in a library with the Dewey Decimal System or granularly by analyzing individual words for grammatical context. If that sounds awesome – it’s because it is.

On the digital spectrum, without text categorization, the internet wouldn’t exist as we know it. You type a word or phrase into your search engine, and it scans the void of the internet to find what you’re looking for. And in a business context, text categorization tools scour digital publications, review sites, blogs, forums, chat logs, social media, etc., to provide structured insights on your topic and inform your market research.

In essence, text categorization is the classification of text-based digital data across themes to provide structure to the incalculable amounts of data across the web. And this is accomplished through advanced AI using natural language processing algorithms.

However, not all analytics tools that use NLP are created equal. Top-tier AI capable of text categorization can differentiate context from similar words and phrases that mean different things. This is essential to verify how consumers feel about your product on social media, for instance.


Even though both sentences include the word “good,” it’s clear to a human reader that the first sentence expresses a negative opinion about the iPhone, whereas the second sentence is very positive. And for tools capable of text categorization, missing the difference here is unacceptable.

Top tools using NLP to analyze text-based data should automatically understand the difference between sentences like these and accurately classify and extract insights – along with much more.

Why is Text Categorization Important?

Text categorization is the foundation that media analytics builds upon. It’s how we can use Boolean search operators to find ultra-specific topics and mentions to create actionable market research. And the ability to extract consumer and market intelligence from unstructured data carries a wealth of benefits for brands.

Modern text categorization goes far beyond the iPhone example above to process language as it’s used in everyday life. For instance, NetBase Quid’s NLP provides robust categorization capabilities, including the following:

  • Reads and interprets the meaning of consumers’ social media opinions with a high level of accuracy.
  • Analyzes and returns data based on all variations of search queries as they occur – in over forty different languages.
  • Processes misspellings, sarcasm, emojis, and brand logos from images to extract information and sentiment. Examples include:
  • Urban words or “slanguage,” for example, “My new phone is sick!”
  • Alternative spellings, for example, “luv,” “kewl,” or “gr8.”
  • Abbreviations like “IMHO” or “ttyl.”
  • Common misspellings such as “the/teh.”

This level of accuracy is essential for brands using analytics tools for consumer and market intelligence. If you’re missing out on social media posts or blogs that contribute to the conversation but have any of the above issues, then you’re working with an incomplete dataset.

In today’s fast-moving market climate, you need accuracy in your market research, which can only be achieved with tools that capture the entire digital conversation. No matter what your brand is trying to research, once you’ve cast a wide net to haul in all the data related to your query, the world is your oyster.

That’s because you can then parse your dataset for a deep understanding of things like customer preferences, passions, and behaviors. Let’s say you want to analyze your brand mentions from across the internet to assess your brand health. In that case, you can slice and dice your dataset to determine top attributes, behaviors, emotions, opinions, competitors named, authors, and much more. It’s the way modern brands read the room and strategize around market conditions and consumer opinion.

Sample Tweet with analysis

Using text categorization in this manner illuminates how consumers and the media feel about aspects of your business. Providing structure where none existed is an invaluable tool for brands to play up their strengths and address problem areas. Without modern text categorization, most consumer and market intelligence would remain unstructured and unseen.

importance of trend analytics

How Text Categorization Helps Brands Excel

With the right tools, brands can harness the power of text categorization to improve their consumer analytics, marketing efforts, and ultimately, their influence within their segment.

Let’s look at a few ways brands can get a leg up.

Real-Time Analysis

Traditional market research methods have gone the way of the dinosaur. That’s because they’re simply too slow to yield practical insights when you need them. Manual analysis is labor-intensive, and in the case of social listening, pretty much impossible.

There are often crucial situations, like a PR crisis, where finding insight into consumer perception needs to happen fast. Text categorization machine learning allows you to follow brand mentions and keywords in real-time so your decision-makers can take action quickly.


Artificial intelligence doesn’t care how much data you need to sift through. Brands big or small can capture and extract insights from vast arrays of data sources in mere minutes. There’s no way that traditional research methods can compete with advanced text categorization tools in terms of speed and scalability. Your AI won’t break a sweat when your brand mentions go through the roof – you can still find quick results.


In contrast to data categorized by human researchers, AI doesn’t get distracted, suffer fatigue, or get bored. And most importantly, it doesn’t exhibit bias. That means your text analysis tools apply the same NLP algorithms to your data every time. As we mentioned earlier, however, not all AI is created equal, so you should be highly critical of the tools you choose to use for your market research. They should be transparent in how they process data – and unquestionably accurate.

Sentiment Analysis

Sentiment reflects how authors feel about your brand or any other topic relevant to your interests. Text categorization tools should provide sentiment analysis by extracting sentiment-based insights (such as likes/dislikes and positive/negative behaviors) and how they relate to your brand. Sentiment analysis should be in every brand toolbox since it informs how people feel about everything from campaign reception to product launches. It’s a critical tool for understanding the voice of the customer and should be used liberally.


Metric Measurement

Text categorization provides the fundamentals behind social listening. As such, your tools should be able to reach into the back corners of the internet to provide counts of different metrics so you can monitor progress over time. For instance, brands can track brand mentions, impressions, net sentiment, passion intensity, engagements, and many more. Metrics provide insight to inform on your brand health, perception, products, and messaging, as well as those of your competitors.

All these attributes of text categorization can be applied to a variety of use cases to help your brand strategize your next move. By capturing consumer and media narratives, brands have the option to dissect the conversations for actionable insights into all of the following areas:

  • Brand Health & Perception
  • Campaign Strategy
  • Product Innovation & Launch
  • Trend Analytics
  • Mergers & Acquisitions
  • Competitive Intelligence
  • Crisis Management
  • Voice of the Customer
  • Influencer & KOL Marketing
  • Technology Scouting

Additionally, when you’re shopping for text categorization tools, make sure they provide the ability to upload your internal datasets for analysis. That way, you have the same ability to extract insights from your reviews, chat logs, customer service call transcripts, etc. And for the cherry on top, reach for tools that excel at data visualization so you can capture insights at a glance and create impactful reports for the C-Suite.

Every brand is surrounded by free-floating data that tells part of their brand story. Brands capturing and analyzing the insights within are the ones capturing share of voice as the digital transformation roars on.

When you’re ready to move to the head of the class with your data analytics, reach out for a demo – we’ve got you covered with world-class text categorization tools.

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