Overview
This lecture covers the basics of text classification in natural language processing (NLP), including techniques, examples, and real-world applications in decision support systems.
Basics of Text Classification
- Text classification assigns pieces of text to mutually exclusive, non-overlapping categories.
- Also known as auto categorization, commonly used in various tasks like sentiment and topic analysis.
- Models are trained to determine which category a document belongs to.
Common Applications
- Sorting chatbot inquiries by topic (e.g., loans, account openings).
- Classifying social media posts to determine appropriate actions.
- Categorizing emails for storage by business-specific schemes.
- Sentiment analysis classifies text as positive or negative.
Approaches to Text Classification
- Lexicon-based: Uses predefined lists of words for each category; scores documents by word occurrences.
- Statistical/Prevalence Score: Assigns weighted scores to words/phrases based on their statistical presence in labeled documents.
- Machine Learning-Based: Uses labeled training data and supervised learning to detect patterns and classify new documents.
Real-World Examples
- Sentiment analysis tools combine categorization and sentiment detection to analyze customer feedback by product aspects.
- Florida State University I.T. used text classification on 100,000 support tickets, identifying main topics and trends over time.
- Classification enabled efficient ticket routing, issue tracking, and improved employee training based on detected trends.
Key Terms & Definitions
- Text Classification — Assigning texts to categories based on content.
- Sentiment Analysis — Determining whether text expresses positive or negative emotion.
- Lexicon-Based Classification — Using dictionaries of category-specific words for scoring.
- Prevalence Score — Statistical weights for words/phrases indicating category likelihood.
- Supervised Machine Learning — Training models with labeled examples to categorize new data.
Action Items / Next Steps
- Review sentiment analysis and classification techniques.
- Explore machine learning methods for automated text categorization.
- Examine real-world applications and consider how text classification can be used in future projects.