Text annotation is the practice of adding notes or glosses to a text, which may include highlights or underlining, comments, footnotes, tags, and links. Text annotation can be used for a variety of purposes, including collaborative writing and editing, commentary, social reading and sharing, and to provide information about a text without fundamentally altering it. In the context of machine learning, text annotation is the process of assigning labels to a digital file or document and its content. Text annotation is a crucial step in preparing accurate training data to train AI models. During the annotation process, a metadata tag is used to mark up characteristics of a dataset, and that data includes tags that highlight criteria such as keywords, phrases, or sentences.

There are different types of text annotation, including sentiment, intent, semantic, relationship, and linguistic annotation. Sentiment annotation evaluates attitudes and emotions behind a text by labeling that text as positive, negative, or neutral. Intent annotation analyzes the need or desire behind a text, classifying it into several categories, such as request, command, or confirmation. Semantic annotation attaches various tags to text that reference concepts and entities, such as people, places, or topics. Entity annotation is the process of assigning entities in text with their corresponding predefined labels based on their semantic meaning. Text classification is the process of annotating an entire body or line of text with a single label. Linguistic annotation involves tagging language data in text or audio recordings to identify and flag grammatical, semantic, or phonetic elements in the text or audio data.

Text annotation is used to teach computers to spot patterns and make predictions in natural language processing (NLP) and machine learning applications. Human annotators are often used to label text data, especially in analyzing sentiment data, as this can often be nuanced and is dependent on modern trends in slang and other uses of language. However, large-scale text annotation and classification tools are available to help achieve the deployment of AI models quickly and efficiently.