Sentiment Analysis
Classifying text as positive, negative, neutral, or any custom sentiment as per customer requirement.
AI Data Operations
Sentiment, intent, entities, dialogue structure, and content safety are labelled by trained domain expert annotators and verified through a multi-stage quality process, so your AI language models learn from data your team can trust.
Capabilities
CAPABILITY / 01
Labeling text at the document, sentence, or message level so models can categorize content correctly at scale.
Classifying text as positive, negative, neutral, or any custom sentiment as per customer requirement.
Identifying and classifying what a user is trying to accomplish in a query, message, or support ticket.
Assigning text to predefined subject categories for routing, search, or content organization.
Labelling the emotional tone expressed in text, beyond simple positive or negative sentiment, including joy, anger, sadness, fear, surprise, disgust, and any custom emotion as per customer requirement.
Categorizing unsolicited, irrelevant, or automated content.
Categorizing text that is abusive, harassing, or violates acceptable use standards.
Categorizing content by type, format, or use case for downstream model training.
HAVE A SPECIFIC REQUIREMENT?
Tell us what your model needs. We’ll help you scope the annotation capabilities that fit your project.
Backed by deep domain expertise across 20+ industry verticals, we collect and annotate data your models can rely on.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.