Automated text classification: discover the different methods and applications

An automatic text classification system can use rules, learn through training, or combine the best of both methods.

The truth is that if you are looking for an advantageous document classification system for your company, you must apply AI-based techniques, such as natural language processing (NLP) to understand the text, and machine learning to optimize the operation and yield fast and accurate results.

To better understand this process, we will talk about what text classification in NLP is, and then we will delve into each of the methods used.

What is text classification in NLP?

Text classification in NLP is the operation that assigns a label (category) to a certain text, in order to automatically group, structure and categorize any type of document, comment, message, invoice, study, file or web content.

For example, a well-known text classification system, although it acts behind the scenes, is the algorithm that filters spam. 

The importance of text classification in NLP

When dealing with large volumes of information, text classification in NLP allows you to quickly identify groups of texts that can be categorized within the same class, even when the topic of each text is different. It can even detect more than one form of categorization or classification of a set of documents.

Text classification allows you to analyze, organize and automatically extract accurate and relevant information for a company or institution. Anything from comments on social networks to surveys or legal documents.

Text classification methods

The various approaches to automatic text classification employ the following three methods:

Rule-based systems

Rule-based text classification systems perform their organizational task based on a hand-crafted set of linguistic rules. Each of these rules provide a predicted category and, together, guide the system to use elements of greater semantic relevance in a text and identify the relevant categories. 

For example, if you want to classify legal documents into two groups: "Criminal Law" and "Commercial Law," the steps would be as follows:

• Define two lists of words associated with each group. A list with words associated with criminal law (crime, penalty, imputation, investigation, etc.) and a list related to commercial law (contract, sale, statutes, transaction, etc.).

• When a new legal document is introduced, the system will count the number of words associated with each list. If it counts more words related to criminal law, the document is classified as "Criminal Law," and vice versa.

  
  
  

Machine Learning systems

Machine Learning text classification systems do not operate with rules. They are algorithms that learn to categorize based on past observations through training with pre-labelled examples. 

These systems learn to recognize associations between text fragments and to assign a certain category (label) to a particular input text. The training process would be as follows:

• Each text is transformed into a certain numerical representation (vector). For example, using the bag-of-words model.

• The classification system is then fed with training data, that is, entering pairs of vectors and labels for each example text. By doing so, the algorithm generates a model for classification.  

A Machine Learning text categorization algorithm must be trained with a sufficient number of samples or examples. It is a more accurate system than rule-based systems and it can always learn how to classify new categories. Among the most used examples of this are:

• Naive Bayes algorithms (statistical algorithms).

• Support vector machines.

• Deep learning algorithms (neural networks). 

Hybrid systems

Hybrid text classification algorithms consist of a base classifier that can be trained (machine learning) and a system that responds to rules. These are classifiers that allow the inclusion of specific linguistic rules to account for labels incorrectly modelled during training. 

After understanding what text classification is, we can move on to what it is useful for.

Interesting content: What is Language Modeling and How Is It Related to NLP?

Why use text classification?

Machine learning text classification and categorization systems are extremely useful in industries that constantly process large amounts of data. For example, they are the perfect solution for:

• Managing business information, such as data related to customer service or HR administration. 

• Classifying financial documents.

• Managing automated assistance.

• Classifying documents and texts in insurance companies.

• Categorizing legal documents, in the legal area.

• Evaluating trends in different areas, such as technology, science or business. 

• Analyzing patents.

The advantages of automatic text classification

Automatic text classification offers these main advantages:

• Provides accurate results: a machine learning text classification system is based on historical data, does not deviate from the target, and maintains information consistency. Therefore, it yields an accurate result, without errors.

• Enables real-time analysis: as a result, business leaders can react immediately to any situation and use timely information to make sound decisions. 

• It is a cost-effective system: thanks to machine learning text classification, it is possible to structure a large number of texts, comments, documents, etc., in a fast and effective way. This saves time, effort and money. 

• Performs opinion mining: this allows companies to extract information from customer reviews, determine the amount of positive and negative comments and find out the acceptance of the product or brand in the market. 

• Improves the reach of advertising and marketing campaigns: text classification with machine learning helps detect a brand's audience by detecting words and phrases used by customers.  

Why choose Pangeanic for text classification?

At Pangeanic, we have our own automatic text classification tool. It is a set of modules with sufficient flexibility to select the type of document format, categorization algorithm or specific document characteristics to be considered. 

Our tool allows you to organize documents using general categories or specific categories that can be set and selected by the user. In addition, our text classification service provides you with:

Data cleansing

Our machine-learning text classification tool ensures the integrity of the processed data. For this, it performs data validation and eliminates incorrect, incomplete or duplicate data. 

Data processing in different languages

Our text classification system can be customized to fit the process, terminology and structure of an organization. In addition, it has the ability to process data in different languages.  

Training data

Our categorization technology is based on Machine and Deep Learning techniques. The training of the algorithm is carried out through a series of model documents associated with each category.

Our Machine Learning text classification and categorization tool is already being used in many companies, financial institutions, research departments and technology centres.

Need to classify your documents? Let's talk! At Pangeanic, we deliver the ideal solution for your company.