Text Classification

This task sorts text into clear buckets—think “great product” labeled “positive” or “enlarge your inbox” flagged “spam” (e.g., sentiment tags, topic bins)—to streamline AI’s grasp of meaning. Our team classifies and organizes data, powering models to sift through vast texts with speed and precision.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are critical in orchestrating the classification and structuring of data for Text Classification within NLP workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce classified datasets that sharpen AI’s interpretive and decision-making capabilities.

Training and Onboarding

PMs design and implement training programs to ensure workers master classification criteria, label consistency, and contextual cues. For example, they might train teams to mark “urgent email” as “priority” or “movie review” as “entertainment,” guided by sample texts and classification rules. Onboarding includes hands-on tasks like assigning labels, feedback loops, and calibration sessions to align outputs with AI processing goals. PMs also establish workflows, such as multi-stage reviews for ambiguous classifications.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., classifying 25,000 text samples) and set metrics like label accuracy, inter-rater agreement, or category coverage. They track progress via dashboards, address classification errors, and refine methods based on worker insights or evolving NLP needs.

Collaboration with AI Teams

PMs connect classifiers with machine learning engineers, translating technical requirements (e.g., balanced classes for sentiment models) into actionable classification tasks. They also manage timelines, ensuring classified datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The classifiers, annotators, or curators perform the detailed work of categorizing and labeling textual datasets for AI training. Their efforts are analytical and systematic, requiring attention to detail and contextual understanding.

Labeling and Tagging

For classification, we might tag text as “negative feedback” or “news article.” In nuanced tasks, they label entries like “spam risk” or “tech topic.”

Contextual Analysis

Our team sorts data, classifying “I love this app” as “positive” or “buy cheap pills” as “spam,” ensuring AI interprets text with relevance and clarity.

Flagging Violations

Workers review datasets, flagging misclassifications (e.g., wrong sentiment) or unclear texts (e.g., vague intent), maintaining dataset quality and consistency.

Edge Case Resolution

We tackle complex cases—like sarcastic tones or mixed topics—often requiring discussion or escalation to classification experts.

We can quickly adapt to and operate within our clients’ NLP platforms, such as proprietary classification tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on the complexity of the text and categories.

Data Volumes Needed to Improve AI

The volume of classified text data required to train and enhance AI systems varies based on the number of categories and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional classification model might require 10,000–50,000 labeled samples per category (e.g., 50,000 tagged reviews for sentiment). For multi-class or imbalanced datasets, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 5,000–15,000 samples per issue (e.g., misclassified spam) are often needed. For instance, refining a model might demand 10,000 new classifications.

Scale for Robustness

Large-scale applications (e.g., enterprise text AI) require datasets in the hundreds of thousands to handle edge cases, rare labels, or diverse texts. A classification effort might start with 100,000 samples, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags uncertain classifications for further labeling. This reduces total volume but requires ongoing effort—perhaps 1,000–5,000 samples weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and accuracy across datasets.

Multilingual & Multicultural Text Classification

We can assist you with text classification across diverse linguistic and cultural landscapes.

Our team is equipped to classify and label text data from global sources, ensuring precise, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: