US-DATAData Annotation
Cabinet
Sentiment analysis enables AI models to determine the emotional tone of a statement and the author's attitude toward an object, event, product or company.
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Sentiment analysis identifies emotional assessment in a message, review, comment or document. Basic labels are positive, negative and neutral; advanced workflows include expanded scales, emotions and intensity.
Positive/negative or satisfied/not satisfied.
Positive, negative, neutral and intermediate ratings.
Joy, anger, fear, disappointment, trust and other emotions.
Assess individual aspects of a product, service or company.
Strength of an emotion or rating, from mild to strong.
Labels can be applied to a document, message, review, sentence, phrase, aspect or target. We can add the overall class, emotion, intensity, target, irony signals and domain attributes.
Guidelines cover irony, sarcasm, implicit negativity, mixed sentiment, negation, comparisons, quoted opinions and multiple targets in one text.
We label reviews, social posts, customer requests, chats, support messages, media, surveys, forums, correspondence and call transcripts. We develop scales, classes and guidelines, run pilots, QA and exports.
Inconsistent labels reduce accuracy, introduce bias and can make production behavior unreliable.
We create task-specific guidelines and validate every stage so the dataset matches the model architecture and business objective.
Examples of data annotation for machine learning
Full data preparation cycle from raw data to model-ready output
How does US-DATA deliver the results business needs?
We pay close attention to quality. Even the most accurate model will not perform well if the data is labeled with errors.
Our team works under a unified annotation rule system based on multi-level review and consistency control. Every process is adapted to client needs and the specifics of the ML model. The result is a clean dataset ready for training without additional rework.
We understand how data quality affects model training.
Annotation tailored to architecture and project goals.
From pilots to large-scale data volumes.
Control at every stage and transparent metrics.
We handle non-standard and challenging scenarios.
Data exported in the format you need.
Faster model training
Higher accuracy and robustness
Lower retraining costs
Production-ready datasets
Data security and compliance
Expandable sections with indicative cost tables.
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* This estimate is not a public offer. Final cost is determined after technical analysis and data review.
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Text sentiment analysis identifies emotional tone, author attitude and rating intensity. US-DATA provides binary and multi-class classification, emotion analysis, aspect-based annotation and domain-specific scales for sentiment analysis.
We develop annotation guidelines, run pilot labeling and multi-level quality control, then deliver a consistent dataset in the format required by your ML pipeline.
The result is data that is ready to train, validate and deploy machine learning models in real operating conditions.
