Crisis Context Mapper — NLP Behavioral Risk Modeling
Traditional bag-of-words text classifiers evaluate crisis statements in isolation, ignoring temporal behavioral transitions and contextual relationship chains critical for understanding distress patterns.
My Role & Contribution
Data Specialist Intern (CSRBOX): Cleaned and preprocessed 101,110 textual records, engineered linguistic features, trained Scikit-Learn classification baselines, and modeled entity-relationship schemas in Neo4j.
Proposed Solution
Cleaned and analyzed 101,110 text records. Beyond training standard Scikit-Learn classifiers, I designed a Neo4j Graph Database schema where statements, anxiety tokens, and sentiment indicators are modeled as interconnected nodes, enabling Cypher graph queries to inspect relationship chains.
- Comprehensive preprocessing and feature engineering on a benchmark dataset of 101,110 text entries
- Comparative evaluation of multiple classification models (Logistic Regression, Random Forest, Gradient Boosting)
- Neo4j graph database schema mapping relationships between statement IDs, sentiment markers, and distress tokens
- Cypher queries enabling researchers to trace relational context pathways across conversational statements
- Responsible educational framing highlighting dataset limitations and clinical non-certification
Architecture & Dataflow
Implementation Status & Roadmap
Implemented
- NLP pipeline with custom tokenization, stopword removal, and TF-IDF vectorization
- Sentiment polarity and subjectivity extraction using Python NLP libraries
- Neo4j graph data model with Cypher queries for relationship exploration
- Scikit-Learn baseline and ensemble classifier benchmarking
Planned Roadmap
- Fine-tuned clinical BERT embeddings for contextual semantic representation
- Temporal graph networks modeling conversational sentiment trajectories
Engineering Trade-offs & Limitations
Rationale:Opted for a graph-relational hybrid workflow over pure end-to-end deep learning. Graph traversal provided human-interpretable relationship paths that allowed team mentors to audit why specific statements were linked to elevated risk indicators.
Results & Evaluation
Successfully processed 101,110 records and demonstrated that graph-augmented relational mapping provides richer contextual insight than isolated keyword matching. Documented strictly as an exploratory data science project, not a clinically deployed crisis response system.