Text Data Mining Tool
A text mining tool with tokenization, frequency analysis, sentiment detection, topic modeling, and keyword extraction. Supports multiple languages, custom stop words, and visualization of text patt...
Target user
Researchers and analysts who extract insights from text data
Problem
Researchers need to mine text data for patterns, sentiment, and topics without setting up complex NLP environments for each text analysis project.
Requirements
- Tokenize and clean text data
- Frequency and n-gram analysis
- Sentiment analysis on text
- Topic modeling with LDA
- Keyword and entity extraction
Core technologies
Python, NLTK, scikit-learn
Completion criteria
Users can mine text for patterns, sentiment, topics, and keywords with visualization of results.
Optional extensions
- Add named entity recognition
- Implement text classification
- Add word cloud generation
- Support multi-language
Project phases
Phase 1: Phase 1: Text Processing
Tasks: Set up Python NLP project, Implement tokenization and cleaning, Add stop word removal, Build frequency analysis
Phase 2: Phase 2: Analysis
Tasks: Implement sentiment analysis, Add topic modeling with LDA, Build keyword extraction, Create entity extraction
Phase 3: Phase 3: Visualization
Tasks: Build frequency charts, Add topic visualization, Create sentiment timeline, Implement word cloud