Text Classification is the processing of labeling or organizing text data into groups. It forms a fundamental part of Natural Language Processing. In the digital age that we live in we are surrounded by text on our social media accounts, in commercials, on websites, Ebooks, etc. The majority of this text data is unstructured, so classifying this data can be extremely useful.
Text Classification has a wide array of applications. Some popular uses are:
Text Classification can be achieved through three main approaches:
These approaches make use of handcrafted
Machine learning approaches
We can use machine learning to train models on large sets of text data to predict categories of new text. To train models, we need to transform text data into numerical data – this is known as feature extraction. Important feature extraction techniques include bag of words and n-grams.
There are several useful machine learning algorithms we can use for text classification. The most popular ones are:
These approaches are a combination of the two algorithms above. They make use of both rule-based and machine learning techniques to model a classifier that can be fine-tuned in certain scenarios.
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