## Implementation approach

To implement the SEO module, we will use the following open-source tools and frameworks:

1. Natural Language Toolkit (NLTK): NLTK is a popular library for natural language processing in Python. We can leverage NLTK to perform various SEO checks on the given text, such as keyword density, readability analysis, and sentiment analysis.

2. Beautiful Soup: Beautiful Soup is a Python library for web scraping. We can use Beautiful Soup to extract relevant information from the given text, such as meta tags, headings, and image alt attributes.

3. PyEnchant: PyEnchant is a spell checking library for Python. We can utilize PyEnchant to check the spelling and grammar of the given text and provide suggestions for improvement.

4. TextBlob: TextBlob is a library for processing textual data. We can use TextBlob to perform part-of-speech tagging, noun phrase extraction, and other linguistic analyses on the given text.

5. Flask: Use Flask for local testing and development purposes. Flask provides a lightweight web framework that allows us to quickly build and test our SEO module.

Overall, by leveraging these open-source tools and frameworks, we can develop a comprehensive and efficient SEO module that meets the requirements and provides valuable insights and suggestions for improving the SEO of the given text.

## Required Python third-party packages

- nltk==3.6.2
- beautifulsoup4==4.9.3
- pyenchant==3.2.1
- textblob==0.15.3
- flask==1.1.2

## Modules

The 'text_processor.py' file contains the TextProcessor class, which is responsible for extracting meta tags, headings, and image alt attributes from the given text.
        
The 'spell_checker.py' file contains the SpellChecker class, which is responsible for checking the spelling and grammar of the given text.
        
The 'seo_checker.py' file contains the SEOChecker class, which is responsible for coordinating the SEO checks by utilizing the TextProcessor and SpellChecker classes.


