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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.

Naive Bayes is a learning algorithm commonly applied to text classification. Some of the applications of the Naive Bayes classifier are: (Automatic) Classification of emails in folders, so incoming email messages go into folders such as: Family, Friends, Updates, Promotions, etc.

The Naive Bayes algorithm. Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

Bayes' Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes' theorem is stated mathematically as follows: Figure created by the author.

Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.

Toxic Comment Classifier is a competition that has been organized by Jigsaw/Conversation AI and hosted on Kaggle. The data set for building the classification model was acquired from the competition site and it included the training set as well as the test set.

Sentiment classification is the automated process of identifying opinions in text and labeling them as positive, negative, or neutral, based on the emotions customers express within them....Sentiment Classification Techniques Rule-Based Systems. Automated Systems (Based on Machine Learning) Hybrid Systems.