What Is Text Classification?

Text classification is the ability of a computer to sort text into predetermined categories. A common example of text classification is the spam filter on your email inbox. An algorithm takes a look at the emails you have coming in and analyzes them to determine whether they should go into your main inbox or your spam inbox. You can also use this technology for many other tasks where you have a large amount of data in text form that can be difficult to extract actionable information from. 

Learn what text classification is, how you can use text classification, and the techniques and algorithms that will help you get more insight from your data. 

What is text classification?

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 it should classify new inputs based on its 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 ultimately determine which is 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 through a lot more data in the same amount of time compared to 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 how you use it, text classification allows you to unlock more insight from your data in less time, reduce human error in tedious categorization tasks, and get real-time data analysis. 

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 makes it possible for 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 like 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 content posted online. 

Types of text classification

You can use different types of text classification techniques and different types of text classification algorithms. Together, 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’s 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 it 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 when you want to get a high-level overview of the topics within a data set. 

Text classification algorithms 

You can also choose from a number of 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 expect to earn and the job outlook in the US. 

AI researchers

Average annual base salary in the US (Glassdoor): $99,680 [1]

Job outlook (projected growth from 2023 to 2033): 26 percent [2]

An AI researcher is a scientific professional who studies artificial intelligence, 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): $148,734 [3]

Job outlook (projected growth from 2023 to 2033): 36 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): $76,131 [5]

Job outlook (projected growth from 2023 to 2033): 8 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' demographic and their needs. In this role, you will present your findings to your client, often using visualizations or other illustrations to demonstrate your work. 

Learn how to implement text classification with Coursera

Text classification is an AI technique that sorts text into categories using techniques like natural language inference, large language models, or probabilistic language modeling. If you want to learn more about text classification or about other ways that AI can help you in your work, consider a course or Specialization on Coursera. 

Consider a Specialization to help you learn how to use AI in your career or to learn skills to start a new career. The Generative AI for Data Scientists Specialization and the Generative AI for Data Analysts Specialization offered by IBM can help you learn skills like AI, generative AI, machine learning, and prompt engineering, but each Specialization is tailored to the skills you’ll need as a data scientist or analyst.