Text Classification

Roughly 90 percent of all data in existence is unstructured, neither adhering to a predefined data model nor a schema framework. In addition to not being easily searchable, unstructured data cannot be stored in a traditional database. This makes the data incredibly difficult, if not impossible, to manually parse and analyze.

Yet that same data contains invaluable insights about a company’s customers, market, competition, and internal operations. It’s, therefore, very much in a business’s best interests to find a way to organize and analyze unstructured data. That’s where artificial intelligence – specifically natural language processing (NLP) – comes in.

What is Text Classification?

Text classification is a machine learning technique for the automatic classification of unstructured text into a set of predefined categories. A text classification algorithm can structure, organize, and categorize virtually any form of text. This could include emails, posts on social media, news articles, medical research, or customer service chat logs.

Text classification is not only a common use case for NLP but also represents one of the model’s fundamental functions. By transforming unstructured text into structured data, a machine can better understand sentiment, semantics, and context. And by combining that functionality with machine learning, a machine can continue to learn and improve through continued processing.

How Does Text Classification Work?

Traditionally, text classification was done entirely by human experts who would interpret content and context in order to label and categorize data. For smaller quantities of data, manual classification could be the superior approach – humans may be able to identify insights and details a text classification model would miss. Unfortunately, the exponential and ongoing growth of unstructured data has rendered this approach obsolete.

Not only is manual text classification time-consuming and expensive, it’s also ineffective. Given the rapid pace at which modern businesses generate new data, there’s no guarantee that the insights generated from manually classified text will still be relevant. In a modern context, automatic text classification is by far the better option.

Although there are many different ways to automate text classification, they ultimately all fall into one of three distinct categories:

• Rule-based
• Machine learning based.
• Hybrid.

Rule-Based Text Classification

A rule-based text classification system operates according to a set of static, predefined rules. Each rule consists of two components:

• A specific set of keywords or phrases known as a pattern.
• The category in which these words or phrases should be associated.

For instance, let’s say you’re a security vendor that wants to categorize blog posts on its website according to industry and solution.

You’ll need to start by identifying common keywords for each category. Possible industries include healthcare, public sector, retail, education, and IT. Products and services might include endpoint management, endpoint detection and response, zero-trust network access, and security consulting.

Next, you’ll need to define rules for each category and set of keywords. Then, when you publish a blog post about a healthcare vendor that utilized your endpoint management and security consulting services, it will be categorized into the appropriate groups.

Rule-based systems are deceptively complex and incredibly time-consuming to set up and configure. They typically require extensive knowledge and testing in order to return accurate results. You must also be cognizant of all pre-existing rules whenever you add a new rule, which makes these systems somewhat challenging to scale.

Still, a rule-based system may be the right option for more straightforward text classification use cases.

Machine Learning-Based Text Classification

This system represents the intersection of text classification and machine learning. Rather than relying on predefined rules, machine learning-based text classifiers are capable of independently identifying connections and correlations between content and category. This is both faster and more accurate than rule-based classifications, with the added benefit of being able to scale with relative ease.

There are many different text classification algorithms one might use, including:

• Naive Bayes algorithms are based on Bayes’s Theorem, a formula for determining the probability of an event based on conditions and factors associated with that event.
• Support Vector Machines algorithms divide their training data into two or more theoretical spaces via lines known as hyperplanes, then map new examples into that same divided space to determine their proper category.
• Deep learning is inspired by the functionality of the human brain and typically takes the form of several interconnected algorithms and architectures known as a neural network.

Hybrid Text Classification

Hybrid systems use a combination of machine learning and rules-based text classification. This effectively provides the best of both worlds, allowing a business to fine-tune its results while enabling greater accuracy and scalability.

Examples of Text Classification

Potential applications of text classification include, but are not limited to:

• Monitoring social media.
• Classifying and categorizing customer reviews
• Content moderation.
• Document classification.
• Language detection.
• Survey response analysis.
• SMS and/or email analysis.

Creating NLP Models for Text Classification

1. Start by defining your keywords and categories/tags.
   1. If you’re leveraging hybrid classification, define your initial ruleset.
2. Either create a training dataset or leverage a premade dataset.
3. Preprocess your text data, removing stop words, replacing sensitive information, and reducing complex words to their base/root form.
4. Transform each piece of text into a numerical representation (or vector), a process known as feature extraction. This can be done either manually or automatically.
5. Create two sets of data – vector-tag pairs and unlabeled pieces of text. The former will be used for training, the latter for evaluation.
6. Start feeding the algorithm the vector-tag pairs.
7. Evaluate the algorithm for accuracy using the unlabeled text data.
8. Repeat steps five and six until the algorithm can begin making accurate predictions and sorting unlabeled text into the proper categories on its own.

If you want to use a programming language for text classification like Python, the process is largely the same. Create or load your data set, pre-process your data, and then train and evaluate your model. There are even several libraries that can make the task easier for you.

Note also that the process above is largely unnecessary if you’re using a deep learning algorithm for text classification. For that, you can rely on unsupervised or semi-supervised machine learning instead.