RegulTermAnalyzer is a Natural Language Processing (NLP) model designed to quickly check and analyze keyword and theme frequencies within regulatory documents from different countries. This tool enables users to track notable trends in regulatory focus and interest, making it valuable for researchers, regulatory bodies, and industry professionals seeking insights into evolving regulatory landscapes.
Features Keyword Frequency Analysis: Identify the frequency of specific terms in regulatory texts from various countries. Cross-Country Comparison: Easily compare term and theme occurrences across multiple countries to identify similarities and differences.
Background As global regulatory frameworks evolve, understanding regional differences in terminology and focus is essential. For instance, the term "real world evidence" has seen increased usage in the United States, reflecting a shift in regulatory priorities. This tool allows users to uncover such trends and provides insights into regulatory approaches in the USA, EU, China, and other regions.
Getting Started Prerequisites To use RegulTermAnalyzer, you'll need:
Python 3.8+ Pandas: For data manipulation Matplotlib: For visualization NLTK or SpaCy: For NLP tasks (optional, depending on the extent of text processing) Any additional libraries required are listed in the requirements.txt file. Installation Clone the repository:
bash Copy code git clone https://github.com/yourusername/RegulTermAnalyzer.git cd RegulTermAnalyzer Install dependencies:
bash Copy code pip install -r requirements.txt Usage Load Your Data: Prepare your regulatory documents in CSV format with columns for country, year, and text.
Run the Analysis: Use the main.py script to perform term and theme frequency analysis. For example:
bash Copy code python main.py --term "real world evidence" You can also specify additional parameters, such as the country or time period:
bash Copy code python main.py --term "real world evidence" --country "USA" --start_year 2010 --end_year 2024 Visualize Trends: The script will generate bar and line charts that illustrate the frequency and trends of the selected terms across countries and over time.
Example Output As illustrated in the example below, RegulTermAnalyzer can reveal trends such as a peak in the term "real world evidence" in the USA in 2022, corresponding with the country's integration of this concept into regulatory practices.
License This project is licensed under the MIT License - see the LICENSE file for details.
References If you use this tool in your research, please cite the relevant paper or reference.