StyloLab

Exploratory Text Analysis with AI & NLP

StyloLab is a personal project focused on structured text analysis and comparison using a combination of classical NLP techniques and modern language model evaluation.
The goal is not to build a polished product, but to explore how to design modular analysis pipelines that are transparent, reproducible, and technically sound.

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🧠 Why StyloLab

Many text analysis tools are either:

• too complex to understand, or
• too shallow to be meaningful

StyloLab bridges that gap by providing a clear and systematic approach to document processing, embedding retrieval, and evaluation of model-assisted analysis.

It demonstrates:

• thoughtful AI system design
• reproducible evaluation pipelines
• modular architecture for experimentation

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🚀 What It Can Do

✔ Load and preprocess text documents
✔ Extract stylistic and semantic features
✔ Combine classical NLP techniques with LLM analysis
✔ Evaluate and compare text outputs
✔ Generate simple visual summaries and reports

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📁 Project Structure

├── app.py # Main entry point
├── features.py 
├── ui.main.py 
├── utils/ # Supporting modules for text extraction and preprocessing
│ ├── chunk_selection.py 
│ ├── craig.py 
│ ├── delta.py 
│ ├── pca_utils.py 
│ ├── plots.py 
│ ├── processing.py 
│ ├── report.py 
│ └── topic_model.py 
├── data/ # Optional sample datasets
├── analysis/ 
│ └── pipeline.py 
├── ui/ 
│ ├── inputs.py 
│ └── sidebar.py 
├── README.md 

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🧩 Design Decisions

StyloLab was designed with clarity, reproducibility, and extensibility in mind. The following principles guided the implementation:

Modular Architecture

The system is structured into clearly separated modules for preprocessing, analysis, and evaluation. This allows individual components to be tested, extended, or replaced without impacting the overall system.

Hybrid NLP Approach

Classical NLP techniques are combined with modern LLM-based methods to balance robustness and flexibility. This avoids unnecessary fine-tuning while still enabling context-aware analysis.

Reproducibility & Stability

Prompt structures, evaluation routines, and configuration choices are kept explicit and versionable. The goal is to produce stable and comparable outputs rather than one-off results.

Practical Focus

StyloLab is built as a working prototype close to real-world usage scenarios, prioritizing maintainability and clarity over experimental complexity.