Text classification, Natural Language Processing(NLP), types and overviews

 

Today we are going to have a look at the Text Classification which is a subset of Natural Language Processing NLP.

In our previous article we talked about Natural Language Processing NLP as an arm of computer science and Artificial Intelligence(AI). 

We looked at the subsets under it which includes Text Classification as number one.

In our next posts,  we shall be looking into these subsets. 

Text classification as we have learnt, is a subfield of Natural Language Processing (NLP) which involves categorizing text into predefined categories or classes. 

Below are some types and overviews of text classification:

1. Binary classification: Is involved  in the classifying text into two categories, such as spam or not spam, positive or negative sentiment, or fake news or real news.

2. Multi-class classification: This also involves classifying text into more than two categories, such as classifying news articles into different topics like sports, politics, or entertainment.

3. Hierarchical classification: This also involves classifying text into a hierarchical structure of categories, such as classifying products on an e-commerce website into categories and subcategories.

4. Rule-based classification: This also involves using a set of predefined rules to classify text, such as using keywords or regular expressions to identify spam emails.

5. Machine learning-based classification: This type of text classification involves the use of machine learning algorithms to learn from labeled data and classify text into categories. Examples of machine learning algorithms used for text classification include Naive Bayes, Support Vector Machines (SVM), and Neural Networks.

Above all, text classification can be said to be a powerful tool for automating the categorization of large amounts of text data, and it has many applications in industries such as marketing, customer service, and content moderation.

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