Citation:

Ankush Singhal, 2021. "Redefining Brand Safety in Programmatic Advertising: Machine Learning Approaches to Content Analysis", ESP Journal of Engineering & Technology Advancements 1(2): 231-243.

Abstract:

Today, digital marketing has transformed, as programmatic advertising has become the new normal, allowing advertisers to deliver their campaigns to the right audiences at scale. Yet, in an ever-changing digital landscape, brand safety remains a challenge. Most traditional brand safety mechanisms fail to do the job of context well, leading to either missed opportunities or inappropriate placements that destroy the brand’s reputation. In this paper, we explore using machine learning to redefine brand safety in programmatic advertising through the process of content analysis. In this work, we analyze the use of Natural Language Processing (NLP), computer vision, and sentiment analysis to gauge content quality and context across various platforms. Through case studies and real-world use, this paper demonstrates how machine learning might create a more nuanced, adaptable, and effective brand safety framework. The findings also highlight the critical need for AI-powered content analysis to protect brands and build consumer trust in digital advertising.

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Keywords:

Programmatic Advertising, Brand Safety, Machine Learning, Content Analysis, Natural Language Processing, Computer Vision, Sentiment Analysis, Contextual Analysis.