Text Data Annotation with Unitlab AI [2025]

The first fact of life: a lot of textual data is generated every day through emails, social media, and other channels.

The second: around 80% of generated data is unstructured, meaning it is not in the form that computers generally understand.

To work with such big data effectively, we first need to train AI/ML models to understand, analyze, interpret, and act on it. This is where text annotation comes in.

This post explores the fundamentals of text labeling, why it matters, its types, and how you can get started with Unitlab AI.

By the end of this article, you'll learn:

• text annotation essentials
• its importance and use cases
• types of text labeling
• how to annotate text

Let's get started!

What is Text Labeling, Essentially?

Text labeling is the process of adding tags, categories, or notes to unstructured textual information. The goal is to make raw words understandable for machines and to train AI/ML to extract patterns from text.

Without this essential feature, natural language processing (NLP) models such as ChatGPT, chatbots, or document readers wouldn't function effectively.

Examples of text annotation:

• Marking names, places, or organizations in a sentence (named entity recognition).
• Categorizing customer reviews as positive, neutral, or negative (text sentiment classification).
• Classifying news reports as finance, sports, or entertainment (text classification).

Essentially, text labeling transforms free-form emails, tweets, and news into structured training data for NLP models.

Why Annotate Text?

You may wonder: why label textual information? Isn't it self-evident? After all, we humans can read and analyze text almost unconsciously.

First, AI/ML systems — chatbots, voice assistants, and self-driving vehicles — are increasingly used in both business and non-business settings. The world is complex, and they require different types of data for training and development.

Second, textual information is not self-describing. The vast majority is unstructured, meaning that for computers it is no different than raw bits in memory, unless categorized and defined.

For these reasons, we annotate text. Here are two concrete examples:

NLP

Almost all texts must be considered with context and in relation to other texts. For example, "I am serious. And don't call me Shirley" makes little sense on its own without background information. NLP is the field of computer science that teaches computers to understand and work with this complexity in human language.

Examples include virtual assistants (Siri, Alexa), search engines (Google, Bing), translation services (Google Translate), and website chatbots. We interact with them daily. The LLMs we use every day — ChatGPT, Gemini, or DeepSeek — are fundamentally based on NLP as well. Their training data is huge, with parameters in the billions.

With large, well-labeled datasets, NLP models can recognize intent, provide meaningful information, and perform accurate translations.

Text labeling powers the creation of ML datasets for these NLP models and applications.

OCR

Optical character recognition (OCR) extracts text from scanned or handwritten images or documents (PDF, JPG, TIFF). It is often used alongside text annotation. While OCR turns images into plain text, text labeling makes that text meaningful for AI/ML models.

Types of Text Annotation

Naturally, we have different intents for our raw textual data. Therefore, different modes of text labeling have emerged to suit needs of AI/ML models.

To illustrate, we'll use this Napoleon Bonaparte quote:

What a novel my life has been! I have fought sixty battles and I have learned nothing which I did not know at the beginning. Look at Alexander, Caesar, Hannibal, and me: we have all founded empires. But on what did these creations depend? On force. But force is transient. Only the ideas that have moved mankind remain.
—Napoleon Bonaparte

Text Entity Detection

This assigns entities in text to predefined labels based on meaning. This may identify entity names, dates, numbers, places, and other arbitrary names. It includes:

• Named entity recognition (NER): Labels key information such as people, places, or dates.

• Relationship annotation: Maps entities in the text that refer to the same object (e.g., I/me).

• Part-of-speech tagging: Identifies grammatical roles, needed for accurate translation and intent recognition. This might sound banal, but machines need to know part of speech of the word to accurately make translations and identify the intent.

Text Classification

While entity detection refers to annotating particular words or phrases in the text, text classification refers to assigning a single label to the whole text. Example: email spam detection labels an email "spam" or "not spam." A news site might classify articles as "Finance," "Politics," or "History."

Our example text could be labeled "History" or "Autobiography."

Text Sentiment Classification

This mode is related to text classification. Instead of assigning labels from a fixed set of categories, it assigns emotional meaning to the whole text — positive, negative, neutral, or mixed. It specifies tone in addition to topic.

Napoleon's quote could be labeled slightly positive or mixed.

Text Sentiment Recognition

This assigns sentiment at the sentence or phrase level. It captures context and nuance more accurately than whole-text sentiment classification. In our case, it could look like this:

Text Language Translation

This translates text into multiple languages, especially where idioms or context matter. Standard services like Google Translate often fail in such cases.

For example, translating Napoleon’s quote to French and Russian shows the need for accurate, context-aware annotation:

We can also combine modes, such as text translation, classification (History), and sentiment classification (Slightly Positive). Elegant, innit?

Use cases of text labeling

Text annotation powers many real-world applications:

• Customer Support: Training chatbots to understand and respond in context of the website.
• Healthcare: Extracting data from medical records or clinical notes and automatically classifying documents for better access and faster search.
• Finance: Flagging fraudulent transactions and identifying money laundering patterns or parsing financial contracts.
• Social Media: Identifying the sentiment of social media posts and tweets to flag potential hate speech, misinformation, or other harmful content for human reviewers.
• Legal: Classifying and translating legal documents and extracting essential data from these contracts.

Demo Project with Unitlab AI

Project Setup

We will explore text labeling hands-on with this tutorial in Unitlab Annotate. First of all, create a free account to follow this tutorial:

After registration, in the Projects pane, click Add a Project:

Name your project, choose Text as the data type and Text Entity Detection as the labeling type:

Upload the project data in the .txt format. The chunk limit specifies the maximum number sentences per chunk and creates chunks accordingly. We set it to 50 for this project.

You can download our sample here:

Then, assign the annotators. By default, it is you. Annotators will receive invitation emails in any case.

Congratulations, you configured your first project successfully.

Labeling Texts

Let's look at our sample text one more time:

What a novel my life has been! I have fought sixty battles and I have learned nothing which I did not know at the beginning. Look at Alexander, Caesar, Hannibal, and me: we have all founded empires. But on what did these creations depend? On force. But force is transient. Only the ideas that have moved mankind remain.
—Napoleon Bonaparte

Depending on the requirements, we could label this quote in a million different ways. To illustrate text annotation, we'll choose the simple way of labeling proper and abstract nouns:

First, we create two classes — Proper and Abstract. We then labeled proper and abstract nouns within our text accordingly.

Dataset Creation

In Unitlab Annotate, you can release datasets once you label your data. The platform will fully take care of dataset versioning, management, and sharing, allowing you to focus on the labeling, not dataset overheads.

Go to the Release Datasets panel and click the Release Dataset button:

By default, the annotation format for texts is JSONL. Because we are under the free plan, our datasets become public inside Unitlab Annotate. You can upgrade to a paid plan for private datasets, along with other features. We choose MIT as the license for our dataset:

After the dataset release, we are redirected to its description page. We can clone, download, and manage it here. You can also manage your datasets in the My Datasets pane:

Conclusion

Text labeling makes raw text usable for AI/ML models. It powers applications from healthcare to finance to law. Different annotation types suit different needs.

You can try text labeling today by creating a free account in Unitlab Annotate. The platform provides everything you need so you can focus on labeling, not overhead.

Explore More

Follow these articles for more on data annotation:

• Audio Data Annotation with Unitlab AI [2025]
• A Comprehensive Guide to Image Annotation Types and Their Applications
• Who is a Data Annotator?

References

• Ritu John (Nov 15, 2024). What is Text Annotation: Types, Techniques, Benefits. DocSumo: Source
• SuperAnnotate (Nov 27, 2023). Text annotation for machine learning [Updated 2024]. SuperAnnotate: Source