Our multi-stage NLP pipeline

Every piece of content passes through a multi-stage NLP pipeline built to extract contextual meaning and stakeholder framing – not just word counts.

Processing Workflow

• Entity recognition and disambiguation: Mapping mentions to unique companies, tickers, executives, and products.
• Topic classification: Multi-level taxonomy covering governance, ESG, operational events, market controversies, and more.
• Contextual sentiment scoring: Measuring tone and intent across financial and reputational dimensions.
• Source weighting: Calibrating impact by credibility, influence, and geographic relevance.
• Cross-language normalization: Aligning sentiment and meaning across diverse linguistic sources.

Each datapoint is timestamped, entity-linked, and updated in real time—enabling time-series tracking, peer benchmarking, and volatility analysis.

Perception Lenses
Atlastic quantifies market-relevant perception across several structured dimensions, including:

• Trust: Confidence in corporate integrity, leadership credibility, stakeholder reliability.
• Risk: Signals of controversy, governance breakdowns, stakeholder pressure, and reputational threats.
• ESG: Dynamic subcategories covering environment, social, labor, ethics, and governance.
• CEO: Perception of leadership actions, decisions, and communications.
• Product: Narratives on product strength, innovation, adoption, and failure.

Built for Reliability
Atlastic’s methodology goes beyond keyword- or frequency-based sentiment tools. The models are:

• Context-aware, capturing narrative framing rather than isolated words.
• Influence-weighted, factoring in source quality and market impact.
• Bias-controlled, through multilingual and multi-market cross-referencing.
• Visibility-separated, distinguishing coverage volume from tone.
• Time-aware, modeling how perception evolves over hours, days, and weeks.