What Is Text Classification?

Key takeaways

Text classification is the process of categorizing text using artificial intelligence (AI) algorithms.

• Text classification is relevant in several careers, such as AI researchers, where you can earn an average annual salary of $102,000 [1].

• Text classification helps to identify emotion within text through sentiment analysis.

• You can use different techniques for text classification, such as natural language processing, probabilistic language techniques, and large language models.

Learn more about what text classification is and how you can use it. If you’re interested in learning more about AI technologies, consider earning an IBM AI Developer Professional Certificate, where you can learn to build generative AI-powered applications and develop skills in large language modeling, computer vision, prompt patterns, and more.

What is text classification in AI?

Text classification is an AI technique that allows you to use an algorithm to sort text data into categories. This technology is an important component that natural language processing builds on to gain a more nuanced understanding of text. Text classification uses predetermined labels, or categories, and different techniques to determine how to classify new inputs based on training. An example of this is using a decision tree to classify text. A decision tree is a deep learning algorithm that makes a series of decisions based on what it can observe about the text to determine the most likely category the input belongs in. 

What can you do with text classification?

Text classification can help you manage tasks and gather insight faster and more accurately. If you collect feedback from your customers or if you get reviews on social media, you likely have a lot of data in the form of text. It’s more time-consuming to read a paragraph of writing and gather data from it than to read a different kind of data, like a rating on a scale of one to five, which can give you a fact at a glance. Text classification can partially automate this process and help you gain more insight from your data. 

Using text classification to automate sorting your text data allows you to sort much more data simultaneously than manual methods. This means you can scale your efforts and sort through other kinds of structured data and documents your company might have, like emails, memos, or legal documents. 

Depending on your use, text classification allows you to quickly unlock more insight from your data, reduce human error in tedious categorization tasks, and get real-time data analysis. 

Why is text classification important?

Text classification enables you to accurately sort through and organize data in real time. This means you can quickly act on the information gathered from text classification, whether to solve a problem or aid in decision-making.

Text classification use cases

You can use text classification in many different ways in both your personal and professional life. For example, you can use text classification to: 

• Analyze emotion behind text: Text classification allows you to perform sentiment analysis, which can help you determine how people feel about your brand when they write reviews or mention you on social media. 

• Prioritizing customer support: You can use text classification to sort your customer support tickets and prioritize the most pressing issues. 

• Look for trends in customer feedback: Text classification makes it easier to look for trends in the customer feedback you receive by sorting each item into categories, such as whether it contains certain ideas or themes. 

• Sort email: Text classification is the basic technology behind spam email filtering. You can also use it to create an automatically sorted email inbox system with files for different types of email. 

• Moderate content: Similar to sentiment analysis, you can use text classification to flag inappropriate or potentially harmful online content. 

Types of text classification

You can use different types of text classification techniques and different types of text classification algorithms. This allows you to customize the exact method you use to classify your data. 

Techniques you can use to classify text include: 

• Natural language inference: This method starts with a proposition statement, such as “I like this product.” The algorithm will sort text based on whether new statements are similar and support the first statement, are neutral to the statement, or contradict the statement. For example, an entailment statement might be, “I use this product every day.” A neutral statement might be. “I enjoy times when I use this product.” A contradiction might look like, “I never use this product.” 

• Large language models: Large language models are deep learning algorithms capable of classifying text using a zero-shot text classification model, which allows the algorithm to sort data it has never seen before.

• Probabilistic language modeling: You can use this method to predict which category an input will belong to. The algorithm works by looking at the words in the text input and calculating the probability that they will belong to any given category. For example, probabilistic modeling would estimate that reviews using words like “fantastic” are highly probable to be positive reviews, although if the word previous is “not,” the probability would change. 

• Bag-of-Words: This technique disregards context, deeper meaning, or word order and simply delivers a count of each word used in the input. This is helpful when looking for overall trends or getting a high-level overview of the topics within a data set. 

Text classification algorithms 

You can also choose from several algorithms that have text classification capabilities, including: 

• Logistic regression

• Naïve Bayes

• Stochastic gradient descent

• K-nearest neighbors

• Decision trees

• Random forest

• Support vector machine

Who uses text classification?

If you’d like to consider a career where you can work with text classification algorithms, a few options to consider are AI researchers, data scientists, and marketing analysts. Explore these careers, including the average salary you can earn and the US job outlook. 

AI researchers

Average annual base salary in the US (Glassdoor): $102,000 [1]

Job outlook (projected growth from 2024 to 2034): 20 percent [2]

An AI researcher is a scientific professional who studies AI, such as machine learning, natural language processing, and computer vision. In this role, you will apply for grants, design and conduct experiments involving AI, publish your findings, or develop prototypes of new AI technologies. As an AI researcher, you may work on AI technologies directly or work on projects that use AI technology to solve interdisciplinary problems. 

Data scientists

Average annual base salary in the US (Glassdoor): $137,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [4]

As a data scientist, you will help businesses or organizations extract meaning from data by collecting, sorting, and analyzing data. You will use algorithms to interact with your data and make predictions based on the existing data. In this role, you may also need to present your findings to senior stakeholders and make recommendations for what they can do with the insight you provide. 

Marketing analysts

Average annual base salary in the US (Glassdoor): $75,000 [5]

Job outlook (projected growth from 2024 to 2034): 7 percent [6]

As a marketing analyst, you will collect, sort, and analyze marketing data to help organizations better understand their market, their products, their competition, and the state of the market in general. You will use algorithms like text classification models and other statistical tools to understand your customers' demographics and needs. In this role, you will present your findings to your client, often using visualizations or other illustrations to demonstrate your work. 

Read more: What Are Machine Learning Models and How Do You Build Them?

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