Text Classification in Natural Language Processing

Text classification is a fundamental task in natural language processing (NLP). It is the process of assigning a label or category to a given piece of text. This can be used for a variety of purposes, such as spam detection, sentiment analysis, and topic modeling.

There are two main types of text classification: supervised and unsupervised.

Supervised text classification involves training a model on a dataset where the labels are already known. This is the most common type of text classification.

Unsupervised text classification does not use labeled data. Instead, the model learns to cluster similar text documents together. This can be used for tasks such as topic modeling.

Text classification is a powerful tool that can be used to make sense of large amounts of text data. It is used in a variety of applications, such as:

• Spam detection
• Sentiment analysis
• Topic modeling
• Question answering
• Machine translation

Text classification is a rapidly growing field with many new research challenges and opportunities.