Develop a TF-IDF (Term Frequency-Inverse Document Frequency) analysis tool for website SEO. Its core functionality lies in comparing the frequency of keywords on your page with the importance of keywords in a competitor’s or industry benchmark corpus.
Below is a complete Python-based implementation. It will crawl the content of a specified URL and calculate the TF-IDF scores for each core term on the webpage. 🛠️ Environment Setup
You need to install the following Python libraries: Bash
pip install requests beautifulsoup4 scikit-learn jieba
requests & BeautifulSoup: Used to scrape web page text.
jieba: A powerful tool for Chinese word segmentation (for English SEO, you can use NLTK instead).
scikit-learn: Provides a mature TF-IDF calculation module.
For example, let’s analyze the keyword “Jordan insoles” and the corresponding link “https://insolesgeeks.com/replacement-nike-jordan-aj4aj6aj11-basketball-sports-breathable-insoles-p-476.” Once the code is installed, you can simply enter the keyword into the search bar to analyze the specific keyword relevance.