What is text classification?
Text classification is the process of assigning predetermined categories to open-ended text documents using artificial intelligence and machine learning (AI/ML) systems. Many organizations have large document archives and business workflows that continually generate documents at scale—like legal documents, contracts, research documents, user-generated data, and email. Text classification is the first step to organize, structure, and categorize this data for further analytics. It allows automatic document labeling and tagging. This saves your organization thousands of hours you'd otherwise need to read, understand, and classify documents manually.
What are the benefits of text classification?
Organizations use text classification models for the following reasons.
Improve accuracy
Text classification models categorize text accurately with little to no additional training. They help organizations overcome errors humans might make when manually classifying textual data. Moreover, a text classification system is more consistent than humans when assigning tags to text data across diverse topics.
Provide real-time analytics
Organizations face time pressure when processing text data in real time. With text classification algorithms, you can retrieve actionable insights from raw data and formulate immediate responses. For example, organizations can use text classification systems to analyze customer feedback and respond to urgent requests immediately.
Scale text classification tasks
Organizations have previously relied on manual or rule-based systems to classify documents. These methods are slow and consume excessive resources. With machine learning text classification, you can expand document categorization efforts across departments more effectively to support organizational growth.
Translate languages
Organizations can use text classifiers for language detection. A text classification model can detect the origin language in conversations or service requests and direct them to the respective team.
What are the use cases of text classification?
Organizations use text classification to improve customer satisfaction, employee productivity, and business outcomes.
Sentiment analysis
Text classification allows organizations to manage their brand effectively on multiple channels by extracting specific words that indicate customer sentiments. Using text classification for sentiment analysis also allows marketing teams to accurately predict purchasing trends with qualitative data.
For example, you can use text classification tools to analyze customer behavior in social media posts, surveys, chat conversations, or other text resources and plan your marketing campaign accordingly.
Content moderation
Businesses grow their audience on community groups, social media, and forums. Regulating user discussions is challenging when relying on human moderators. With a text classification model, you can automatically detect words, phrases, or content that might breach the community guidelines. This allows you to take immediate action and ensure conversations happen in a safe and well-regulated environment.
Document management
Many organizations face challenges in processing and sorting documents to support business operations. A text classifier can detect missing information, extract specific keywords, and identify semantic relationships. You can use text classification systems to label and sort documents like messages, reviews, and contracts into their respective categories.
Customer support
Customers expect timely and accurate responses when they seek help from support teams. A machine learning-powered text classifier allows the customer support team to route incoming requests to appropriate personnel. For example, the text classifier detects the word exchange in the support ticket and sends the request to the warranty department.
What are the approaches to text classification?
Text classification has evolved tremendously as a subset of natural language processing. We share several approaches that machine learning engineers use to classify text data.
Natural language inference
Natural language inference determines the relationship between a hypothesis and a premise by labeling them as entailment, contradiction, or neutral. Entailment describes a logical relationship between the premise and hypothesis, while contradiction shows a disconnect between textual entities. Neutral is applied when neither entailment nor contradiction is found.
For example, consider the following premise:
Our team was the winner of the football championship.
These are how different hypotheses would be tagged by a natural language inference classifier.
- Entailment: Our team likes playing sports.
- Contradiction: We are people who don't work out.
- Neutral: We emerged as the football champion.
Probabilistic language modeling
Probabilistic language modeling is a statistical approach that language models use to predict the next word when given a sequence of words. Using this approach, the model assigns a probabilistic value to each word and calculates the likelihood of the following words. When applied to text classification, probabilistic language modeling categorizes documents based on specific phrases found in the text.
Word embeddings
Word embeddings are a technique that applies numerical representations to words that capture their semantic relationships. A word embedding is the numerical equivalent of a word. Machine learning algorithms cannot analyze text efficiently in their original forms. With word embeddings, language modeling algorithms can compare different texts by their embeddings.
To use word embeddings, you must train an natural language processing (NLP) model. During the training, the model assigns related words with numerical representations closely positioned in a multi-dimensional space known as vector semantics.
For example, when vectorizing text with embeddings, you will find dogs and cats closer to each other in a two-dimensional vector space than tomatoes, people, and rocks. You can use the vector semantics to identify similar text in unfamiliar data and predict subsequent phrases. This approach is helpful in sentiment classification, document organization, and other text classification tasks.
Large language models
Large language models (LLMs) are deep learning algorithms trained on massive volumes of text data. They are based on the transformer architecture, a neural network with multiple hidden layers capable of processing text data in parallel. Large language models are more powerful than simpler models and excel at various natural language processing tasks, including text classification.
Unlike their predecessors, large language models can classify text without prior training. They use zero-shot classification, a method that allows the model to categorize unseen text data into predefined categories. For example, you can deploy a zero-shot text classification model on Amazon Sagemaker Jumpstart to sort new year's resolutions posts into career, health, finance, and other classes.
How do you evaluate text classification performance?
Before you deploy text classifiers for business applications, you must evaluate them to ensure they don’t suffer from underfitting. Underfitting is a phenomenon where the machine learning algorithm performs well in training but fails to classify real-world data accurately. To evaluate a text classification model, we use the cross-validation method.
Cross-validation
Cross-validation is a model evaluation technique that splits the training data into smaller groups. Each group is then divided into samples for training and validating the model. The model first trains with the allocated sample and is tested with the remaining sample. Then, we compare the model's result with those annotated by humans.
Assessment criteria
We can evaluate the text classification model from the assessment on several criteria.
- Accuracy describes how many correct predictions the text classifier made compared to total predictions.
- Precision reflects the model's ability to consistently predict a specific class correctly. A text classifier is more precise when it produces fewer false positives.
- Recall measures the model's consistency in successfully predicting the right class compared to all positive predictions.
- The F1 score calculates the harmonic mean of precision and recall to provide a balanced overview of the model's accuracy.
How do you implement text classification?
You can build, train, and deploy a text classification model by following these steps.
Curate a training dataset
Preparing a high-quality dataset is important when training or fine-tuning a language model for text classification. A diverse and labeled dataset allows the model to learn to identify specific words, phrases, or patterns and their respective categories efficiently.
Prepare the dataset
Machine learning models can't learn from raw datasets. Therefore, you must clean and prepare the dataset with preprocessing methods like tokenization. Tokenization divides each word or sentence into smaller parts called tokens.
After tokenization, you should remove redundant, duplicate, and abnormal data from the training dataset because it may affect model performance. You then split the dataset into training and validation data.
Train the text classification model
Choose and train a language model with the prepared dataset. During training, the model learns from the annotated dataset and tries to classify text into its respective categories. Training is complete when the model consistently converges to the same outcome.
Evaluate and optimize
Assess the model with the test dataset. Compare the model's precision, accuracy, recall, and F1 score with established benchmarks. The trained model may require further fine-tuning to address overfitting and other performance issues. Optimize the model until you achieve satisfactory results.
What are the challenges in text classification?
Organizations can use commercial or publicly available text classification resources to implement text classifier neural networks. However, limited data can make curating training datasets challenging in certain industries. For example, healthcare companies may need help sourcing medical datasets to train a classifying model.
Training and fine-tuning a machine learning model is costly and time-consuming. Moreover, the model may overfit or underfit, causing inconsistent performance in actual use cases.
You can build a text classifier with open-source machine learning libraries. However, you need specialized machine learning knowledge and years of software development experience to train, program, and integrate the classifier with enterprise applications.
How can AWS help with your text classification requirements?
Amazon Comprehend is a NLP service that uses machine learning to uncover valuable insights and connections in text. The Custom Classification API lets you easily build custom text classification models using your business-specific labels without learning ML.
For example, your customer support organization can use Custom Classification to automatically categorize inbound requests by problem type based on how the customer has described the issue. With your custom model, it is easy to moderate website comments, triage customer feedback, and organize workgroup documents.
Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy ML models for any use case. It has fully managed infrastructure, tools, and workflows.
With Amazon SageMaker JumpStart, you can access pretrained and foundation models (FMs) and customize them for your use case with your data. SageMaker JumpStart provides one-click, end-to-end solutions for many common ML use cases. You can use it for text classification, document summarization, handwriting recognition, relationship extraction, question and answering, and filling in missing values in tabular records.
Get started with text classification on Amazon Web Services (AWS) by creating an account today.
