Generally, for any class of NLP problems, you first apply text preprocessing and feature extraction techniques. Once you have reduced the noise in the text and are able to extract features out of text, you perform various machine learning algorithms to solve different NLP classes of NLP problems. In this section, we will cover one such problem, called text classification.
Text classification is one of the very common use cases of NLP. Text classification can be used for use cases such as email SPAM detection, identifying retail product hierarchy, and sentiment analysis. This process is typically a classification problem wherein we are trying to identify a specific topic from a natural language source of a large volume of data. Within each of the data groups, we may have multiple topics discussed and hence it is important to classify the article or the textual information into logical groups. Text classification techniques help us to do that.
These...