A Literature Review on the Role of Machine Learning, Deep Learning and AI in Real-Time Ad Personalization and Sentiment Analysis on Social Media Platforms
Keywords:
Machine Learning, Deep Learning, Reinforcement Learning, Artificial Intelligence, Large Language ModelsAbstract
The present literature review presents the use of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Artificial Intelligence (AI), and Large Language Models (LLMs) for advertising, where the author mentions real-time personalized advertisement and sentiment analysis of social media platform, especially Facebook. These advanced technologies altogether have currently continued to transform the landscape of digital advertising, thereby empowering advertisers realized engagement, target enhancement, and high return-on-investment (ROI) value. The real-time personalization, upon validation, will employ large amounts of data from social media on user data to target their advertisements towards users' preferences, behaviour, and previous interactions. At the same time, AI-powered sentiment analysis would enable the analysis of user-generated versus user emotions, allowing for improvements on marketing message content and analysis of advertisement effectiveness.
In the review, these technological improvements are dedicated to extolling these technologies' efficiencies; however, the author discusses the bottlenecks and challenges that hinder the fullest realization of these technologies by issues surrounding privacy, algorithmic neutrality, and scalability. This is paired with a discussion on the ethics of utilizing personal data for targeting advertisements and underscores the importance of transparent, responsible AI. This paper hopes to extend further insight into the present status and the future of AI-driven advertising strategies by performing an in-depth study on the subjects of application, benefits, and challenges of ML, DL, RL, AI, and LLMs employed in Facebook advertising.
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