NLP Analysis of Official Documents
Budget: ₹12,500 – ₹37,500 INR
I am building an AI-driven workflow that performs deep data analysis on large volumes of official documents. The text must be ingested, cleaned, and processed so I can uncover patterns, key entities, and actionable insights that would otherwise stay hidden in lengthy reports, regulations, and policy papers.
Core requirements
• End-to-end text pipeline: ingestion (PDF, DOCX, plain text), preprocessing, language detection if needed, tokenisation, stop-word removal, and lemmatisation.
• Exploratory analysis and visual summaries: word-frequency plots, topic trends over time, and sentiment distribution where relevant.
• Advanced NLP: named-entity recognition, key-phrase extraction, and topic modelling (LDA or BERTopic).
• Result delivery: a concise written report plus reusable Python notebooks/scripts and clearly commented source code, ready to run on my local environment (Python 3.10, pandas, spaCy, scikit-learn, PyTorch/Transformers as appropriate).
Acceptance criteria
1. The pipeline processes at least 1 GB of sample official documents without manual intervention.
2. Visual outputs render correctly in Jupyter and export to PNG/PDF.
3. Code follows PEP 8 and includes a README explaining setup, parameters, and how to extend the model.
Once these steps are met I can integrate the solution with my existing analytics stack.
Core requirements
• End-to-end text pipeline: ingestion (PDF, DOCX, plain text), preprocessing, language detection if needed, tokenisation, stop-word removal, and lemmatisation.
• Exploratory analysis and visual summaries: word-frequency plots, topic trends over time, and sentiment distribution where relevant.
• Advanced NLP: named-entity recognition, key-phrase extraction, and topic modelling (LDA or BERTopic).
• Result delivery: a concise written report plus reusable Python notebooks/scripts and clearly commented source code, ready to run on my local environment (Python 3.10, pandas, spaCy, scikit-learn, PyTorch/Transformers as appropriate).
Acceptance criteria
1. The pipeline processes at least 1 GB of sample official documents without manual intervention.
2. Visual outputs render correctly in Jupyter and export to PNG/PDF.
3. Code follows PEP 8 and includes a README explaining setup, parameters, and how to extend the model.
Once these steps are met I can integrate the solution with my existing analytics stack.