Agentic Sentiment Analysis Monitoring System

Money Group

Problem:

The organization relied on manual sentiment analysis across multiple communication and social platforms, creating significant operational bottlenecks and analytical inconsistencies.

Manual processes were labor-intensive, slow to deliver insights, and prone to subjective interpretation, preventing timely response to emerging sentiment trends or customer issues.

The lack of scalable, automated analysis limited the organization's ability to monitor brand perception, customer satisfaction, and market sentiment at scale across diverse platforms.

Solution:

Engineered an intelligent, end-to-end sentiment monitoring system that automated data collection, analysis, and insight delivery across multiple platforms.
Architected n8n workflows to orchestrate data ingestion from unstructured data sources, implementing API connectors and webhook triggers for real-time data streaming.
Integrated advanced NLP and large language models (LLMs) to perform nuanced sentiment classification, emotion detection, and contextual analysis, moving beyond simple positive/negative/neutral categorization to extract actionable business intelligence.
Designed automated routing and alerting mechanisms that surfaced high-priority insights to relevant stakeholders in near-real time.

Results:

Achieved 90% accuracy in sentiment classification, significantly outperforming manual analysis consistency and enabling reliable insights for decision-making.
Accelerated decision-making velocity by 85%, transforming sentiment analysis from a slow, retrospective exercise to a real-time intelligence feed that enabled rapid response to emerging trends and issues.
Reduced manual analysis effort by 80%, eliminating the need for large-scale manual review teams and reallocating those resources to higher-value strategic analysis.
Established continuous sentiment monitoring capability across platforms, enabling proactive brand management, customer experience optimization, and early detection of reputational risks.

Technical Highlights:

Configured n8n automation workflows handling multi-stage data pipelines: ingestion, preprocessing, enrichment, and delivery.
Integrated multiple API connectors to stream data from diverse platforms (social media, review sites, customer feedback channels) with error handling and retry logic.
Implemented NLP processing layers leveraging transformer-based models for sophisticated sentiment and emotion analysis.
Integrated LLM APIs for contextual interpretation, enabling the system to understand nuance, sarcasm, and domain-specific language beyond traditional sentiment scoring.
Built intelligent routing logic to categorize insights by urgency, topic, and stakeholder relevance.
Developed real-time dashboards and alert mechanisms surfacing anomalies and high-impact sentiment signals.

Agentic Sentiment Analysis Monitoring System

Money Group

Problem:

Solution:

Results:

Technical Highlights:

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