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Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category.

The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations.

Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity & Online Abuse Detection. Detecting Trends in Customer Feedback. Urgency Detection in Customer Support.

The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations.

Another example of text classification used for a wide variety of tasks is language detection. These classifiers are able to detect the language used in text data and perform various tasks, for example: Automatically route customer support tickets to localized teams. Sort through documents based on their language.

Abstract: As more and more sources publish a large amount of news on the Internet every day, it is necessary to categorize news articles so that users can effectively obtain information.

There are five news categories i.e. Sports, Business, Politics, Entertainment, Tech.

Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category.