Text

Text data is a sequence of characters, words, or tokens that convey information in a human-readable format.

It is used when there is a need to analyze or process natural language data, such as documents, emails, or social media posts. Text data is commonly applied in scenarios such as sentiment analysis, text classification, text generation, and language translation. Working with text data involves converting the text into a format that can be processed by machine learning models, often involving steps like tokenization and stemming.

Tokenization is the process of breaking down text into smaller units called tokens, which can be words, phrases, or characters. For example, the sentence "Machine learning is fascinating" can be tokenized into ["Machine", "learning", "is", "fascinating"].

Stemming is the process of reducing words to their base or root form. For example, the words "running", "runner", and "ran" can be reduced to the root word "run".

Text data is important because it allows for the analysis and understanding of natural language, enabling models to extract meaningful insights from textual information. It is a powerful approach in machine learning, enabling models to process and analyze human language effectively.