Sentiment Analysis & Narrative Tracking Pipeline

The Agent

System Architecture: This system utilizes a Multi-Agent Orchestration model hosted on FastAPI. The primary agent manages the preprocessing and linguistic analysis of datasets (up to 5MB), while a secondary agent handles the dynamic visualization of sentiment trends. To ensure ethical data handling, the pipeline implements a local Regex-based Anonymization Layer to scrub PII before analysis, ensuring that the tracking of emotional "tonal architecture" remains secure and compliant.

Methodology: The Contextual Nuance Engine

The Sentiment Analysis Pipeline uses a Multi-Layered NLP Protocol that goes beyond simple keyword matching. It is engineered to detect the "tonal architecture" of complex text, accounting for the linguistic subtext that standard models often miss.

• Valence & Arousal Mapping: The agent measures both Valence (the direction of sentiment) and Arousal (the emotional intensity). This allows the system to distinguish between "quiet contentment" and "high-energy excitement."

• Negation & Sarcasm Detection: By leveraging Dependency Parsing, the pipeline understands how modifiers like "not" or "never" flip the sentiment of a sentence, helping prevent the "false positives" common in basic sentiment tools.

• Narrative Tracking: The system identifies recurring "emotional clusters" over time, allowing users to visualize how brand perception or social narratives evolve through different phases of a dataset.

Technical Architecture: The Research Benchmarking Protocol

Sentiment analysis is a rapidly evolving field, with modern models shifting from traditional lexicon-based approaches to advanced Transformer architectures such as BERT and RoBERTa. Maintaining accuracy requires constant benchmarking against the latest peer-reviewed research.

The Solution: This agent implements User Search Sovereignty via a Search-Path Protocol. Every analysis concludes with a "Relevant Resources & Technical Benchmarks" button. The system intercepts analyzed themes to generate live, high-precision search queries across the ArXiv, Papers With Code, and the Stanford NLP Group archives. This ensures immediate access to the latest research on sentiment accuracy, transformer-based techniques, and bias mitigation in NLP.

Prompt Guide for High-Value Results

To generate the most accurate and actionable emotional data, use prompts that focus on comparative analysis and thematic subtext:

• The "Brand Perception" Prompt: "Analyze the sentiment shift in these reviews before and after the update, focusing on whether 'frustration' has moved toward 'resolution'."

• The "Rhetorical Subtext" Prompt: "Examine this speech for hidden negative sentiment. How is formal language used to mask underlying aggression or exclusionary rhetoric?"

• The "Thematic Cluster" Prompt: "Identify the core emotional drivers in this dataset. Are positive sentiments driven by 'Price,' 'Reliability,' or 'Customer Service'?"

Explainer

Technical Insight: This Jupyter Notebook demonstrates the raw research phase of the pipeline, specifically the testing of the Valence-Arousal logic and narrative clustering. The Live Agent (above) represents the transition to a production-ready, multi-agent system capable of real-time data visualization and secure API lifecycle management.

Click here to see the Sentiment Preprocessing Repository | Click here to see the Sentiment Graphing Repository