Classification is the process of predicting the class of given data points. Classes are referred to as targets/ labels or categories. Classification belongs to the category of supervised learning.

There are two types of learning techniques in Classification –

• Lazy Learning
• Eager Learning

Textual Classification

Textual classification is one of the basic tasks in Natural Language Processing (NLP).

It is a technique used to organize, structure, and categorize textual content like documents, reports, emails, website content, and files.

Textual classification finds a wide range of applications in areas like sentiment analysis, spam detection, and labeling of information.

Example:

• News snippets can be classified according to sports, business, entertainment, and politics.
• Brand reviews can be classified according to positive, negative, and neutral sentiments.

Rubiscape provides four Textual Classification algorithms in Text Analytics:

• Adaboost
• Maximum Entropy
• Naïve Bayes
• Support Vector Machine (SVM)