Automated Text Classification with Machine Learning

 

In the era of digitization, information availability online has witnessed an exponential surge. The internet is brimming with textual content—be it emails, web pages, news, learning content, or journals—and this calls for effective ways to read, analyze and report this information efficiently. Text analysis is an integral aspect across several verticals, including marketing, product development, academia, and governance, and, yet, there’s always room to make it more in-depth and conclusive.

This is where automatic text classification using machine learning steps in. It entails implementing a set of statistical techniques in the form of a comprehensive model to identify intent, emotion, sentiments, and other aspects related to text. This model can further be applied to other text in order for it to gain more insights and broaden its learning capabilities; this is known as supervised learning. Alternatively, the model may also comprise a large set of algorithms that can be applied across vast data sets to extract meaning, which is known as unsupervised learning.

Understanding the difference between supervised and unsupervised learning is the key to gaining the best of both in one system. This article will explain the nuances of both the learning techniques in the context of text classification, while also walking you through custom text classification.

 

Supervised Text Classification

Supervised text classification involves subjecting the algorithm to predefined classification categories to train the algorithm. The algorithm is trained on the labeled dataset and then tested with unobserved data, which the algorithm classifies into respective categories based on its training.

Spam filtering of emails is one such example of supervised classification, where the emails are automatically classified based on their content. Analysis of language, sentiment, intent, and emotion is performed using supervised learning models. Generally, supervised learning algorithms are used to address problems where text has to be extracted and classified from a vast data dump—consider it similar to finding a needle in a haystack. Naturally, it is but imperative that the algorithms yield extremely accurate results, which means the training must be rigorous and as precise as possible, involving special loss functions, sampling, and multiple classifiers working closely together.

 

Unsupervised Text Classification

In unsupervised text classification, no training data is provided to the algorithm. The algorithm classifies text based on natural patterns; note that natural patterns needn’t necessarily be logical. The algorithm searches for similar structures and patterns in the data points and groups them accordingly. Search engines are the prime examples of unsupervised text classification—the algorithm builds clusters based on the search term and accordingly presents the results.

Since unsupervised text classification doesn’t require predefined classification categories, there’s no need to train the algorithm. This comes in handy when generating unspecific insights about any given textual data. Moreover, as there is no training involved, unsupervised classification is highly flexible and language-agnostic.

 

Custom Text Classification

Often, the lack of a predefined dataset is one of the biggest hindrances to leveraging machine learning for automated text classification. You may want to use AI to categorize data, but it requires you to make a predefined dataset—a chicken and egg problem if you may. This is where the need for custom text classification arises.

In its latest research work, ParallelDots has proposed a method to implement zero-shot learning on text, where the algorithm is trained to learn relationships between sentences and their categories on a large noisy dataset. This can be further used to generalize new categories and even datasets if you’re lucky enough. ParallelDots calls this paradigm Train Once, Test Anywhere. Multiple neural networks can also leverage this training methodology to get almost accurate results on different datasets.

In a nutshell, machine learning and AI are posed redefine IT as we know it. It is but natural for developers, system admins, and anyone working in IT to develop a good understanding of the subjects. If you want to learn more about how you can leverage machine learning for different use cases, you can explore Hands-On Machine Learning with C#. The book helps develop an intuitive understanding of various concepts, the techniques of machine learning, and various available machine learning tools through which users can add intelligent features such as image and motion detection, Bayes intuition, deep learning and belief, and so on to C# .NET applications.

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