Many methods exist to generate content, including natural language generation from numerical data. Despite all the attractiveness of computational linguistics, our work will lead us to generate content through summarization algorithms.
Offered by Google Cloud’s Natural Language APIs, entity and sentiment analyses classify terms and extract a general feeling. Dated 2015, a Google patent entitled “Rankings of search results based on entity metrics” demonstrates the value of using it in our SEO actions.
Developed by engineers assigned to the Google Brain team within Google’s Artificial Intelligence division, TensorFlow is an open source framework (since 2015) dedicated to machine learning. It is one of the most widely used tools in the field of machine learning.
Without the use of log analysis, one sometimes strives to optimize a page without it being explored or too little to produce a tangible result. Log analysis provides information accessible nowhere else, and guides SEO actions on factual elements.
Several patents have been filed by Google to quantify the opinions and reviews of Internet users from corpus that do not use traditional rating systems. The sentiment analysis then takes on its full meaning: how do we quantify so-called “raw” opinions?
In order to be able to predict position changes after possible on-page optimisation measures, we trained a machine learning model with keyword data and on-page optimisation factors. With the help of this model, we can now automatically analyse thousands of potential keywords and select the ones that we have good chances on reaching interesting rankings for, with just a few simple on-page optimisations.