Hyper-personalization Through Long-Term Sentiment Tracking in User Behavior: A Literature Review

Abstract

Hyper-personalization, the process of tailoring suitable or personable content, products and experiences to individual users, has become increasingly intelligent and sophisticated through advancements in artificial intelligence (AI) and natural language processing (NLP). This literature review focuses on the niche area of long-term sentiment tracking to enhance hyper-personalization. By examining modern methodologies, applications, and ethical implications, the review underscores how sentiment analysis over time facilitates deeper understanding of user behavior, facilitating more effective engagement. The review also acknowledges challenges such as data privacy, sentiment drift, and algorithmic bias, providing a roadmap for future research directions.