{"id":22,"date":"2020-05-29T12:57:26","date_gmt":"2020-05-29T12:57:26","guid":{"rendered":"https:\/\/wordlift.io\/ng\/?p=22"},"modified":"2020-05-29T13:10:04","modified_gmt":"2020-05-29T13:10:04","slug":"serp-analysis-with-ai","status":"publish","type":"post","link":"https:\/\/wordlift.io\/ng\/serp-analysis-with-ai\/","title":{"rendered":"SERP Analysis with the help of AI"},"content":{"rendered":"\n

SERP analysis<\/strong> is an essential step in the process of content optimization to outrank the competition<\/em> on Google. In this blog post I will share a new way to run SERP analysis using machine learning<\/strong> and a simple python program that you can run on Google Colab.\u00a0<\/p>\n\n\n\n

\"\"<\/td>Jump directly to the code: Google SERP Analysis using Natural Language Processing<\/a><\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

SERP (Search Engine Result Page) analysis is part of keyword research and helps you understand if the query that you identified is relevant for your business goals. More importantly by analyzing how results are organized we can understand how Google is interpreting a specific query. <\/p>\n\n\n\n

What is the intention of the user<\/strong> making that search?<\/strong><\/p>

What search intent Google is associating with that particular query<\/strong>?<\/p><\/blockquote>\n\n\n\n

The investigative work required to analyze the top results provide an answer to these questions and guide us to improve (or create) the content that best fit the searcher. <\/p>\n\n\n\n

While there is an abundance of keyword research tools that provide SERP analysis functionalities, my particular interest lies in understanding the semantic data layer that Google uses to rank results<\/strong> and what can be inferred using natural language understanding<\/strong> from the corpus of results behind a query. This might also shed some light on how Google does fact extraction and verification for its own knowledge graph starting from the content we write on webpages. <\/p>\n\n\n\n

Falling down the rabbit hole<\/h2>\n\n\n\n

It all started when Jason Barnard<\/a> and I started to chat about E-A-T and what technique marketers could use to \u201cread and visualize\u201d Brand SERPs<\/a><\/strong>. Jason is a brilliant mind and has a profound understanding of Google\u2019s algorithms, he has been studying, tracking and analyzing Brand SERPs since 2013. While Brand SERPs are a category on their own the process of interpreting search results remains the same whether you are comparing the personal brands of \u201cAndrea Volpini<\/em>\u201d and \u201cJason Barnard<\/em>\u201d or analyzing the different shades of meaning between \u201cmaking homemade pizza<\/em>\u201d and \u201cmake pizza at home<\/em>\u201d. <\/p>\n\n\n\n

Hands-on with SERP analysis<\/h2>\n\n\n\n

In this pytude<\/a> (simple python program) as Peter Norvig would call it, the plan goes as follow:<\/p>\n\n\n\n