Text classification is a machine learning task that involves assigning predefined labels to text data in order to automatically categorize it into groups. As businesses and platforms deal with ever-growing volumes of unstructured text, text classification provides a powerful way to organize, interpret and act on text data at scale.
Today’s organizations produce a massive amount of text data across websites, apps and other networks in the form of customer reviews, social media posts, legal documents, emails and more. There are insights buried in this data that could help the organization make better decisions. Text classification is the first step of the process.
A support ticket that is labeled “urgent” can be routed to a prioritized workflow. An email labeled “spam” can be automatically archived. A customer review labeled “positive” can inform a customer sentiment report about a new product. Classified data can be aggregated and visualized in order to uncover trends and patterns that would otherwise remain hidden.
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Text classification is a fundamental task in natural language processing (NLP), used in a wide range of applications. A text classifier is a machine learning model that solves different classification problems, like classifying texts by topic, sentiment or intent. Here’s how it works:
Supervised models are typically used to perform text classification. The first step is to gather a large dataset of text samples. These could be emails, social posts, customer reviews or documents.
Human annotators apply a label to each piece of text. For example, “spam” or “not spam,” or “positive” vs. “negative” sentiment. This labeled training dataset forms the foundation for training a machine learning model. Typically, the more data, the more accurate outputs.
Pre-processing the input text transforms text into a standardized, machine-readable format. Classifiers can only work with text that’s been translated into numerical representations, often using word embeddings or more advanced encoder architectures that capture the semantic meaning of language.
Hyperparameters configure variables like the number of neural network layers, the number of neurons per layer or the use of an activation function. These hyperparameters are chosen before training begins.
Then data is fed into a classification algorithm, which learns to associate patterns in the data with their associated labels.
Text classification algorithms include:
Support vector machines
Deep neural networks
The trained model is tested on a separate validation or test dataset to evaluate model performance with metrics such as accuracy, precision, recall and F1 score, and evaluated against established benchmarks.
A well-performing text classification model can be integrated into production systems where it classifies incoming text in real time.
Advanced models can improve over time by incorporating new data and retraining. Pretrained language models like BERT have already learned a deep understanding of language and can be fine-tuned on specific classification tasks with relatively little data. Fine-tuning reduces training time and boosts performance, especially for complex or nuanced categories.
While supervised methods are far more common, models can be trained without labeled data using unsupervised learning. Instead of being told the correct category for each text, the model tries to find structure or patterns in the data on its own. This contrasts with supervised text classification, where each training example is labeled with a predefined categorization. Supervised methods are far more common.
For example, with a technique called clustering, the model groups similar pieces of text into clusters based on shared features, which can then be interpreted as a category.
Here are some common NLP tasks involving classification:
Spam detection systems analyze incoming messages and classify them as either “spam” or “not spam.” They use a mix of rules, statistical patterns and machine learning techniques to detect phishing emails, bulk marketing messages from unknown senders, suspicious links, malware and more.
Sentiment analysis is the process of analyzing large volumes of text to determine its sentiment. Sentiment analysis helps organizations determine whether people have positive or negative associations at digital touchpoints.
A machine learning algorithm can gauge sentiment using words that appear in text as well as the order in which they appear. Developers use sentiment analysis algorithms to teach software how to identify emotion in text similarly to the way humans do.
The goal of topic classification is to assign predefined topic categories to a piece of text. It's commonly used in content management, aggregation, academic research and customer feedback analysis to organize large volumes of unstructured text.
While topic classification tells you what a message is about, intent detection tells you what the user is trying to do. Intent detection is useful for automating conversations and routing tasks in customer service or e-commerce. Without it, systems would struggle to provide meaningful assistance.
Toxicity and abuse detection is a text classification task that focuses on identifying and flagging harmful, offensive or abusive content online. This might includes language that is hateful, threatening, harassing, obscene or otherwise inappropriate. Large social media platforms use classification algorithms to assist their support staff in managing huge global user bases.
There are many open-source tools available for building text classifiers. Frameworks like TensorFlow and PyTorch offer components for creating and training models. For instance, a TensorFlow-based classifier might use a Keras API with modules like validation_data, optimizer and loss to train a model on labeled data. PyTorch, a Python-based machine learning library known for its flexibility is also widely used with utils like DataLoader and nn.Module.
While traditional classifiers use fixed labels, the rise of large language models (LLMs) has introduced generative approaches to classification. Models can be prompted to produce both labels and explanations in natural language. For instance, one could prompt an LLM with a sentence and ask it to classify the sentiment, generate a justification or suggest similar categories—all without additional training.
With GPU acceleration, training times are drastically reduced, especially for large datasets or complex deep learning architectures. Researchers and developers often share their training pipelines and models on GitHub.