Sentiment Aware Programmatic Advertising: Integrating NLP into Real Time Bidding Systems

Abstract

This article examines how sentiment aware programmatic advertising can be designed by integrating natural language processing into real time bidding systems for digital display advertising. Building on a systematic review of research on programmatic auctions, user response prediction, and sentiment based marketing analytics, the article synthesizes evidence that current architectures rely heavily on behavioural histories and coarse contextual features while largely ignoring fine grained affective information in page content and user generated text. The review identifies a persistent gap between advances in sentiment analysis, which provide accurate polarity and emotion signals, and real time bidding practice, where such signals are rarely embedded directly in bidding logic. In response, the article proposes a conceptual framework in which sentiment and other affective text features enter user response models and bidding functions as core inputs rather than auxiliary indicators. The findings highlight implications for advertising relevance, brand safety, computational efficiency, ethical governance of affective data, industry adoption, and rigorous future empirical evaluation efforts.