Sentiment-Driven Management Dynamics Prototype
Budget: ₹100 – ₹400 INR
I’m putting together a small R&D–style project that blends text-based sentiment analysis with a lightweight system-dynamics model to show how attitudes expressed in project documents can feed back into overall performance metrics.
Scope
• Focus on management challenges within the energy or construction sector (I’m flexible here as long as the management angle is clear).
• Work exclusively with an openly available dataset—press releases, project reports, stakeholder comments, or similar text sources.
• Build a sentiment classifier (Python, scikit-learn / PyTorch / TensorFlow—your call) and connect its output to a causal-loop or stock-and-flow model created in Vensim, AnyLogic, or a Python equivalent. The dynamic model should illustrate how changing sentiment influences key project-performance variables.
Deliverables
1. Cleaned and documented public dataset with acquisition script.
2. Well-commented code notebook(s) for sentiment training, validation, and inference.
3. System-dynamics model with clear parameters and a short write-up explaining the feedback structure.
4. A concise report (or slide deck) that walks through methodology, novelty of approach, and performance results.
Acceptance
The work is considered complete when I can run the notebooks end-to-end on my machine, reproduce the sentiment scores, and observe at least one meaningful management insight from the dynamics simulation.
If you’re confident you can merge NLP with system-thinking and enjoy experimenting with public data, I’d love to see how you’d tackle this.
Scope
• Focus on management challenges within the energy or construction sector (I’m flexible here as long as the management angle is clear).
• Work exclusively with an openly available dataset—press releases, project reports, stakeholder comments, or similar text sources.
• Build a sentiment classifier (Python, scikit-learn / PyTorch / TensorFlow—your call) and connect its output to a causal-loop or stock-and-flow model created in Vensim, AnyLogic, or a Python equivalent. The dynamic model should illustrate how changing sentiment influences key project-performance variables.
Deliverables
1. Cleaned and documented public dataset with acquisition script.
2. Well-commented code notebook(s) for sentiment training, validation, and inference.
3. System-dynamics model with clear parameters and a short write-up explaining the feedback structure.
4. A concise report (or slide deck) that walks through methodology, novelty of approach, and performance results.
Acceptance
The work is considered complete when I can run the notebooks end-to-end on my machine, reproduce the sentiment scores, and observe at least one meaningful management insight from the dynamics simulation.
If you’re confident you can merge NLP with system-thinking and enjoy experimenting with public data, I’d love to see how you’d tackle this.