> For the complete documentation index, see [llms.txt](https://labelfi-1.gitbook.io/labelfi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://labelfi-1.gitbook.io/labelfi/basics/labelfi-data-labeling-services/4.3-text-annotation.md).

# 4.3 Text Annotation

Text annotation involves labeling textual data to make it useful for natural language processing (NLP) applications. Labelfi provides efficient workflows for text annotation, such as:

* Named Entity Recognition (NER): Identifying key entities within the text, such as names of people, locations, or organizations, enabling a better understanding of the structure and meaning of the text \[26].
* Sentiment Analysis: Classifying text based on the expressed sentiment, which can be essential for analyzing customer reviews, social media, and other user-generated content.
* Text Classification: Categorizing text into predefined categories, making it easier to structure large datasets for tasks like spam detection, topic categorization, and document retrieval \[12]\[26].

Through AI-powered tools, Labelfi ensures that the text annotation process is both scalable and accurate, helping create high-quality datasets for NLP tasks.