This Python-based plagiarism detection system compares multiple documents using TF-IDF and semantic analysis techniques to identify similarities and potential instances of plagiarism.
- Compares multiple documents for similarities
- Uses both TF-IDF and semantic analysis methods
- Flags potential plagiarism based on a customizable threshold
- Generates detailed similarity reports
- Produces similarity heatmaps for visual analysis
- Calculates document uniqueness scores
- Exports results to CSV files for further analysis
- Python 3.x
- NLTK
- scikit-learn
- matplotlib
- seaborn
- pandas
- sentence-transformers
- Clone the repository or download the script.
- Install the required packages:
pip install nltk scikit-learn matplotlib seaborn pandas sentence-transformers
- Download required NLTK data:
import nltknltk.download('stopwords')nltk.download('punkt')-nltk.download('wordnet')
Add your documents to the documents list in the script.
Set the plagiarism_threshold as desired (default is 70%).
Run the script:
python plagiarism_detection.py
- Console output with similarity scores and potential plagiarism flags
- TF-IDF and semantic similarity reports (saved as text files)
- Similarity heatmaps (displayed and can be saved)
- CSV files with detailed similarity results
- Document uniqueness scores
- Adjust the plagiarism_threshold to change sensitivity
- Modify the preprocess_text function to customize text preprocessing
- Change the semantic model in SentenceTransformer() for different embedding results
- This system is for educational purposes and should not be solely relied upon for detecting plagiarism in academic or professional settings.
- Contributions to improve Plagiarism-Detection-System are welcome. Please follow the standard fork-and-pull request workflow.