NLP with Python and TextBlob Training Course

Introduction

• Overview of TextBlob features and architecture for government use
• Fundamentals of Natural Language Processing (NLP)

Getting Started

• Installing TextBlob for government applications
• Importing necessary libraries and data

Building Text Classification Models

• Loading data and creating classifiers for government projects
• Evaluating the performance of classifiers
• Updating classifiers with new data for ongoing accuracy
• Utilizing feature extractors to enhance model effectiveness

Performing NLP Tasks using TextBlob

• Tokenization for government text analysis
• Integration with WordNet for enhanced semantic understanding
• Noun phrase extraction for detailed content analysis
• Part-of-speech tagging to identify grammatical structures
• Sentiment analysis for gauging public opinion and feedback
• Spelling correction to maintain document integrity
• Translation and language detection for multilingual data processing

APIs and Advanced Implementations

• Custom sentiment analyzers for government-specific contexts
• Tokenizers tailored for government documents
• Noun phrase chunkers to extract meaningful phrases from text
• Part-of-speech (POS) taggers optimized for official language use
• Parsers for complex sentence structure analysis
• Blobber for comprehensive text processing tasks

Troubleshooting

Summary and Next Steps