Topic Modeling & Keyword Extraction

This task uncovers the pulse of text—think an article tagged with “climate change” and “renewable energy” or a blog distilled to “travel tips” (e.g., themes like “tech trends,” keywords like “AI”)—to map content’s core ideas. Our team extracts and organizes these signals, enabling AI to tag and sort with sharp relevance.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the identification and curation of data for Topic Modeling & Keyword Extraction within NLP workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce topic and keyword datasets that enhance AI’s content understanding and categorization.

Training and Onboarding

PMs design and implement training programs to ensure workers master topic identification, keyword selection, and contextual relevance. For example, they might train teams to spot “healthcare policy” in news or pull “blockchain” from tech posts, guided by sample corpora and extraction frameworks. Onboarding includes hands-on tasks like tagging topics, feedback loops, and calibration sessions to align outputs with AI discovery goals. PMs also establish workflows, such as multi-layer reviews for nuanced themes.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., extracting from 20,000 documents) and set metrics like topic coherence, keyword precision, or relevance score. They track progress via dashboards, address extraction gaps, and refine methods based on worker insights or evolving content needs.

Collaboration with AI Teams

PMs connect extractors with machine learning engineers, translating technical requirements (e.g., high LDA scores for topics) into actionable curation tasks. They also manage timelines, ensuring topic and keyword datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The extractors, taggers, or analysts perform the detailed work of identifying and structuring topic and keyword datasets for AI training. Their efforts are analytical and thematic, requiring precision and content awareness.

Labeling and Tagging

For topic data, we might tag text as “economic growth” or “sports highlights.” In keyword tasks, they label terms like “machine learning” or “sustainability.”

Contextual Analysis

Our team pinpoints themes, tagging “AI ethics” in a debate or pulling “remote work” from blogs, ensuring AI grasps the essence of sprawling texts.

Flagging Violations

Workers review datasets, flagging off-topic labels (e.g., irrelevant themes) or weak keywords (e.g., generic terms), maintaining dataset quality and focus.

Edge Case Resolution

We tackle complex cases—like overlapping topics or niche jargon—often requiring deeper analysis or escalation to content experts.

We can quickly adapt to and operate within our clients’ NLP platforms, such as proprietary topic 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 extraction goals.

Data Volumes Needed to Improve AI

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

Baseline Training

A functional topic model might require 5,000–20,000 tagged samples per category (e.g., 20,000 documents with topics). For diverse or dense corpora, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 samples per issue (e.g., vague topics) are often needed. For instance, refining a model might demand 5,000 new extractions.

Scale for Robustness

Large-scale applications (e.g., enterprise content AI) require datasets in the hundreds of thousands to handle edge cases, rare topics, or varied terms. An extraction 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 unclear topics for further tagging. This reduces total volume but requires ongoing effort—perhaps 500–2,000 samples weekly—to sustain quality.

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

Multilingual & Multicultural Topic Modeling & Keyword Extraction

We can assist you with topic modeling and keyword extraction across diverse linguistic and cultural landscapes.

Our team is equipped to identify and tag text data from global sources, ensuring thematic, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: