BSc IT Project Guide: Text Preprocessing Tool
1. Project Title
Text Preprocessing Tool to tokenize, stem, and clean text data
2. Objective
The objective of this project is to develop a web or desktop-based application that performs text preprocessing tasks such as tokenization, stemming, stop word removal, and cleaning. This is essential for preparing text data for natural language processing (NLP) and machine learning applications.
3. Tools & Technologies
- Programming Language: Python
- Framework: Flask or Streamlit (for web), Tkinter (for desktop)
- Libraries: NLTK, spaCy, re, Pandas
- Database: SQLite (optional)
4. Functional Requirements
- User can input raw text.
- Tool should perform tokenization.
- Implement stemming and lemmatization.
- Remove stop words.
- Clean text by removing punctuation, special characters, and converting to lowercase.
- Display the cleaned and processed text.
5. Non-Functional Requirements
- User-friendly interface
- Fast and efficient processing
- Accurate preprocessing with optional NLP customization
6. System Design
- Input Interface: Text input area
- Processing Module: Applies selected preprocessing techniques
- Output Interface: Shows processed text
7. Implementation Plan
Week 1-2: Requirement Analysis and Design
Week 3-4: Setup Environment and Basic UI
Week 5-6: Implement Tokenization and Cleaning
Week 7-8: Implement Stemming and Stop Word Removal
Week 9: Testing and Debugging
Week 10: Documentation and Final Report
8. Expected Outcome
A functional text preprocessing tool that prepares raw textual data for further analysis, such as sentiment analysis or classification.
9. Future Enhancements
- Add support for multiple languages
- Integration with larger NLP pipelines
- Export processed text to CSV or JSON