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Training Data offers Text Labeling Services, providing accurate and comprehensive labeling of text data to enhance natural language processing (NLP) models and text-based applications across various industries and use cases. Our expert annotators meticulously annotate text data with relevant labels, categories, or tags, ensuring high-quality training data for machine learning models and improving text understanding and analysis capabilities.

What is Text Labeling?

Text labeling in data training services involves the process of annotating textual data with descriptive labels, categories, or tags to classify, categorize, or extract meaningful information from the text. This annotated data serves as ground truth for training machine learning models to understand, interpret, and process text for various natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, text classification, and information extraction.

Types of Text Labeling Services


Sentiment Analysis Labeling

Sentiment analysis labeling entails annotating text data with sentiment labels, such as positive, negative, or neutral, to categorize the sentiment or opinion conveyed in the text. Such labeling is valuable for assessing public sentiment toward products, services, or events by analyzing customer reviews, social media posts, and feedback.

Named Entity Recognition (NER) Labeling

Named Entity Recognition labeling entails identifying and tagging named entities, such as persons, organizations, locations, dates, and other entities, within text data. This type of annotation is crucial for extracting structured information from unstructured text, aiding tasks such as information retrieval, entity linking, and knowledge graph construction.

Text Classification Labeling

Text classification labeling entails categorizing text data into predefined categories or classes based on their content or topic. This annotation type is useful for organizing and indexing large text corpora, powering content recommendation systems, and automating document categorization tasks in various domains, such as news categorization, spam detection, and topic modeling.

Intent Detection Labeling

Intent detection labeling entails identifying the underlying intent or purpose behind user queries or statements within text data. This annotation type is crucial for building conversational AI systems, chatbots, and virtual assistants that can understand and respond to user intents accurately in natural language interactions.

Text Summarization Labeling

Text summarization labeling involves generating concise summaries or abstracts of longer text documents or passages. This annotation type is useful for extracting key information, identifying important points, and condensing lengthy texts into digestible summaries, facilitating information retrieval and document summarization tasks.

Entity Sentiment Analysis Labeling

Entity Sentiment Analysis labeling combines named entity recognition (NER) with sentiment analysis, annotating text data with sentiment labels specific to identified entities within the text. This annotation type enables fine-grained sentiment analysis at the entity level, providing insights into the sentiment expressed towards different entities mentioned in the text.

Topic Modeling Labeling

Topic modeling labeling involves clustering text data into topics or themes based on the underlying patterns and relationships in the text content. This annotation type is useful for uncovering hidden themes, trends, and patterns in large text corpora, supporting tasks such as content organization, trend analysis, and document clustering.

Emotion Detection Labeling

Emotion detection labeling involves annotating text data with emotion labels, such as happiness, sadness, anger, or fear, to identify the emotional state or sentiment conveyed by the text. This annotation type is valuable for understanding user emotions, sentiment analysis in social media, and emotional intelligence applications.

Intent Classification Labeling

Intent classification labeling involves categorizing user queries or utterances into specific intents or actions based on their semantic meaning. This annotation type is essential for building natural language understanding (NLU) systems that can accurately interpret user intent and perform appropriate actions or responses in conversational interfaces and chatbot applications.

How we Deliver Text Labeling Projects

At Training Data, we follow a systematic approach to deliver Text Labeling Projects with precision, accuracy, and efficiency. Our process comprises several key stages, each meticulously designed to ensure high-quality annotations and client satisfaction.

Project Discovery and Scoping

/ 01
We start by working closely with our clients to grasp their project requirements, objectives, and the particular labeling tasks involved. This phase involves gathering detailed information about the text data, annotation guidelines, and desired outcomes to define the scope of the project.

Data Collection and Preparation

/ 02
After defining the project scope, we gather the text data needed for labeling and preprocess it as required. This may involve data cleaning, formatting, and normalization to ensure consistency and quality in the annotation process.

Annotation Methodology Selection

/ 03
Based on the project requirements and text data characteristics, we select the most suitable annotation methodologies and tools. Whether it involves sentiment analysis labeling, named entity recognition (NER), or text classification labeling, we choose the optimal approach to achieve accurate and consistent annotations.

Annotation Execution

/ 04
Our team of experienced annotators meticulously label the text data according to the predefined guidelines and criteria. This involves identifying and tagging entities, sentiments, or intents within the text, ensuring the annotations capture the relevant information for downstream tasks.

Quality Control and Assurance

/ 05
Quality is paramount in our process. Before finalizing the annotations, we conduct rigorous quality control checks to detect and rectify any errors or inconsistencies. This includes manual inspections, inter-annotator agreement checks, and automated validation tools to ensure data integrity.

Validation and Review

/ 06
Once the annotations are completed, we conduct thorough validation and review processes. We verify the accuracy and completeness of the annotations, ensuring they align with the client's specifications and meet industry standards. Any discrepancies or issues identified are promptly addressed.

Delivery and Formatting

/ 07
Upon validation, we deliver the annotated text data in the client's preferred format and specifications. Whether it's structured data formats, JSON files, or database exports, we ensure the deliverables are compatible with the client's systems and workflows for seamless integration.

Client Feedback and Iteration

/ 08
We value client feedback throughout the process. We encourage clients to review the delivered annotations and provide any necessary revisions or adjustments. Our goal is to ensure the final deliverables meet or exceed the client's expectations and requirements.

Post-Delivery Support

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Our support doesn't end with delivery. If clients have any questions or require further assistance, our team is readily available to provide ongoing support and guidance. We strive to be a trusted partner in leveraging annotated text data for our clients' projects and initiatives.

Text Labeling Use Cases

Customer Support and Chatbots

Companies use text labeling data to train chatbots and virtual assistants to understand and respond to customer inquiries, complaints, and requests. Labels such as intents, entities, and sentiments enable chatbots to provide personalized and accurate responses, improving customer service efficiency and satisfaction.

Social Media Analysis

Marketing firms leverage text labeling data to analyze social media content for sentiment analysis, trend detection, and brand monitoring. Labels such as sentiment, topics, and user intents enable companies to understand public opinion, track brand perception, and identify emerging trends in real-time.

Market Research and Surveys

Market research companies utilize text labeling data to analyze survey responses, customer feedback, and product reviews for sentiment analysis and trend identification. Labels such as sentiments, themes, and product attributes enable companies to gather actionable insights, identify market opportunities, and make data-driven decisions.

Legal and Compliance

Law firms and regulatory agencies use text labeling data to categorize and analyze legal documents, contracts, and regulatory texts for compliance monitoring and risk assessment. Labels such as legal clauses, contract terms, and regulatory requirements enable companies to identify potential risks, ensure compliance, and mitigate legal liabilities.

Content Moderation and Safety

Online platforms employ text labeling data to moderate user-generated content for inappropriate or harmful material, such as hate speech, spam, and abusive language. Labels such as toxicity levels, offensive language, and content categories enable companies to maintain a safe and inclusive online environment for users.

E-commerce and Product Reviews

E-commerce platforms use text labeling data to analyze product reviews, ratings, and user comments for sentiment analysis and product categorization. Labels such as product features, user sentiments, and review ratings enable companies to understand customer preferences, improve product offerings, and optimize marketing strategies.

Medical and Healthcare

Healthcare providers utilize text labeling data to analyze electronic health records (EHRs), clinical notes, and medical transcripts for information extraction and decision support. Labels such as medical conditions, treatments, and patient demographics enable clinicians to access relevant patient information quickly, improve diagnosis accuracy, and personalize treatment plans.

Human Resources and Recruitment

HR departments use text labeling data to analyze resumes, job descriptions, and candidate profiles for talent acquisition and recruitment purposes. Labels such as skills, qualifications, and job roles enable companies to match candidates with suitable job opportunities, streamline the hiring process, and identify top talent effectively.

Financial Services and Fraud Detection

Financial institutions leverage text labeling data to analyze transaction records, financial statements, and customer communications for fraud detection and risk management. Labels such as suspicious activities, transaction types, and account statuses enable companies to detect fraudulent behavior, prevent financial losses, and ensure regulatory compliance.

Education and Academic Research

Educational institutions use text labeling data to analyze academic papers, research articles, and educational materials for content categorization and knowledge extraction. Labels such as topics, concepts, and citation types enable researchers and educators to organize information, identify research trends, and facilitate knowledge dissemination.

Stages of work

  • Application

    Leave a request on the website for a free consultation with an expert. Th e acco unt manager will guide you on the services, timelines, and price
  • Free pilot

    We will conduct a test pilot project for you and provide a golden set, based on which we will determine the final technical requirements and approve project metrics
  • Agreement

    We prepare a contract and all necessary documentation upon the request of your accountants and lawyers
  • Workflow customization

    We form a pool of suitable tools and assign an experienced manager who will be in touch with you regarding all project details
  • Quality control

    Data uploads for verification are done iteratively, allowing your team to review and approve collected/annotated data
  • Post-payment

    You pay for the work after receiving the data in agreed quality and quantity


  • 24 hours
  • 24 hours
  • 1 to 3 days
  • 1 to 5 days
    Conducting a pilot
  • 1 day to several years
    Carrying out work on the project
  • 1 to 5 days
    Quality control
You pay for the work after you have received the data
in the established quality and quantity

Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

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