Short Text Classification: A Quick Guide | Levity

In the age of information overload and shortened attention spans, short texts are quickly becoming the go-to content for many people.

The billions of Tweets, chats, Instagram posts, online reviews, and more, prove that short text is here to stay. To thrive in this information age, you need a way to efficiently understand and act on short text.

Short text classification aims to overcome the many challenges of extracting meaningful insights from short texts. It combines Machine Learning and data science to create a classification model capable of handling the nuances of human language.

In this article, you’ll learn more about short text classification and find useful tips for creating your own short text classifier, making the process a little less time-consuming and tedious.

Shall we?

What is short text classification?

Short text classification is a method of classifying short pieces of text, such as Tweets, Facebook posts, online reviews, and more. It uses Machine Learning, Natural Language Processing (NLP), and Deep Learning methods to help create meaningful and relevant categories from small chunks of text data.

Short text classification is more accurate than traditional text classification methods such as word embedding, which uses a conventional bag-of-words (BOW) model to group similar words in a data set.

For example, word embeds may assume that since apples and bananas are both fruits, they should be categorized as such. This method does nothing more than just label data and doesn’t reveal deeper relationships between words and their overall context.

Short text classifiers use advanced classification models and algorithms like decision trees, support vector machines, and naive Bayes classifiers to understand semantic relationships between different texts beyond the traditional approach. Their ability to find more context in the text improves the classification’s accuracy.

Businesses use such a data classification method to optimize workflows, moderate content, monitor social media, and much more by analyzing the short-text data they have available to them.

What are the challenges of short text classification?

Short text is exactly that—short. It can be difficult to interpret because you don't have enough context to work with. It may not have a clear intent or convey a recognizable message—making it difficult to classify.

Short texts are often unstructured and can contain spelling and grammatical errors. The general use of informal tone, slang, and abbreviations makes them even more complex.

When dealing with short text, categorizing a few words and calling it a day isn’t enough. To classify text properly, you need clean data. While short text conveys a message in a few words, given how incoherent and error-prone it is, some Machine Learning techniques can have difficulty dealing with it.

The advantages of short text classification with Natural Language Processing

Overcoming the challenges of short text classification offers great benefits to businesses. Using Natural Language Processing (NLP) techniques to analyze and categorize short texts allows businesses to gain deeper insights from their data—be it for customer service or product development.

It’s fast and efficient, and insights can be used to make data-driven decisions across your organization. Plus, NLP classifiers are easy to test, evaluate, and improve with the right tools. No-Code AI solutions enable businesses to use NLP to understand and classify short texts—and avoid the need for a costly AI engineering team.

How to build a No-Code short text classifier

Using a No-Code short text classifier is the best way to go if you’re just starting out with short text classification. Building your own text classifier is challenging, especially if you don’t have the right technical skillset.

No-code AI tools—like Levity—make short text classification a whole lot easier. They enable organizations to use Artificial Intelligence and Machine Learning models to categorize short text automatically. They also enable businesses to extract meaning from short text using methods like NLP for sentiment analysis.