Machine Learning Techniques for Web Page Classification with Search Engine Optimization

다국어 초록 (Multilingual Abstract)

Automated Search Engine Optimization (SEO) is crucial for streamlining processes, ensuring consistency, and adapting to changes, thereby enhancing a website's overall success and visibility in the competitive online landscape. This research introduces a dataset and a baseline method for classifying website SEO ranks into three categories. Using 26 keywords, data was collected from 780 web pages across various Google rankings, and 36 ranking factors were employed to predict their rank. Key considerations for webpage preparation include anchor text, backlinks, Ref Domain, unique visits, and text length. The Random Forest model exhibited superior performance, achieving an average accuracy of 72% in predicting actual search rankings. The significance of this automated approach lies in identifying web pages requiring SEO improvements, leading to enhanced search engine rankings.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Works
• 3. Methodology
• 4. Result and Discussion
• 5. Conclusion
• Acknowledgments
• 6. References