Impact of Digital Advertising Based on Natural Language Processing Technology

Abstract

In this study, we explored the applications that are used for NLP for digital ads. This research explores numerous technical developments, primarily Big Data, IoT, Large Accessible Online Courses (MOOCs), Digital Automation, and their anticipated long-term technological, social, and environmental ramifications. We represent the effect on Facebook using NLP and AMT of content ads and consumer interaction. To take into account the connection between various forms of social-media advertisement material and client involvement as Likes remarks, deals, and the snapshot of tweets, we use content code 106,316 FB messages in 782 companies using an articulation of Turkish Mechanical and Language Procedure Algorithms from the Amazon. The consideration of commonly used content that is synonymous with the character of the company as comedy and feeling have to do with higher levels of shopper participation with a tweet. These results continue after the convergence of remedies to concentrate non-irregularly on the Edge Rank (News Feed) algorithm on Facebook, thereby representing more carefully the reaction of our customers to content rather than the social focus of Facebook. Our findings indicate that content architecture has advantages by mixing enlightening characteristics that receive immediate guidance with material connected to a brand character that has future access and social character networking platform utilizing improved engagement.