# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 3 Related Work
In [28] the authors conducted a detailed literature review of SEO factors that affect rankings on SERP. They listed 24 website characteristics that were determined as important factors, including on-page and off-page factors. In the on-page factors' group, keywords in the title tag and keyword density in various parts of a web page were identified as the most important factors. Of 14 SEO factors used in [29] only two were off-page (backlinks), while among the on-page factors, eight were exactly the same as the ones used in our research. In general, there is a great deal of agreement between authors in this field regarding the choice of on-page factors used in research on SEO. In [30] the authors used experts' evaluation by means of an interview and the survey method to find out significant website SEO factors. The results of their research showed that meta tags are the factor that has the most significant influence on search engine ranking, followed by keywords in content and the website design. The in-depth analysis of ranking factors is presented in the master thesis [31]. Similar to our research, the author conducted a survey on a large number of digital marketing companies exploring their opinion on ranking factors. Results showed that the page level keyword frequency-related factors occupied the third and fourth places (out of 8) in terms of their importance for web page ranking (with link-related factors in the first place and social metrics in the last place). In our research, we also based extraction of relevant factors on the experts, but instead of surveys we used machine learning classification algorithms on manually labeled web pages. The PhD thesis [32] also focused on SEO factors for increasing the visibility of websites. The primary method in this thesis was an experimental website case study for the purpose of which an on-page SEO model was built based on the SEO techniques that were successful in the past. The techniques used in this research also included keyword-based methods in various parts of a web page and HTML code. Article [33] explored web page classification methods and usage scenarios where a machine learning algorithm was employed to classify web pages in predefined categories based on features extracted from text and HTML tags. Results suggested that HTML tags play an important role in methods based on keyword frequencies to identify web page topics, and that they are understudied in classification. Similar problems were addressed in [34], which explored classification of web pages that is language independent. They proposed a deep learning algorithm that overcomes the language barrier and successfully classifies web pages based on their function. In [35] the authors used machine learning algorithms to predict web page rank in SERP for pages in the e-commerce gift industry. They researched 30 blogs in the selected industry that occupied first-page Google ranking. They tested two machine learning models (LightGBM and XGBoost) and conducted feature analysis, which showed that the features that had most impact are links, domain security, and H3 headings, while other keyword-related frequencies were not shown as significant in this research. The factors explored are very similar to the ones used in our research: length of text; counts of H1, H2, and H3 tags; their length; and keyword frequencies in tags, URL, and meta tags. Web page classification for various purposes is an extensive field of study. One of the most popular categorization tasks is web spam classification [15; 16; 17]. Features' extraction for this task is also a topic of much research [36]. This study contributes to the field of web page classification by exploring classification based on SEO recommendations--which is understudied. Unethical, or \"black hat\", SEO techniques are outlined in [18].