The effect of Bert-based grammatical analysis on Google search results

Abstract

This study aims to study the BERT, namely Bidirectional Encoder Representations from Transformers model, which is introduced by Google and is of great importance in content analysis, and to examine the role of grammatical accuracy in the process of content quality measurement and Search Engine Results Pages (SERP). BERT has an important role among the algorithms used by Google in order to maintain the quality of search results and to provide more relevant content to users by understanding the content more effectively. In this study, CoLA data, which is accepted as the most reliable data in this field and therefore used frequently in similar BERT studies, is used. The main purpose here is to make a BERT-based grammatical evaluation of sentences in a content and then examine these results on pages with optimal ranking values, to examine the connection between search results and grammatical accuracy and the importance of this parameter. In this context, the project consists of two phases. In the first phase, the content of the pages that are visible in the first 20 in 50 different queries are scored with the pre-trained BERT model. In the second phase, a dataset that includes different SEO-focused metrics of the same pages is created manually, and the importance of the BERT score among these features is investigated.