Mining User Behavior Patterns in Online Social Networks: A Review of Recent Advances

Abstract

This paper reviews the recent advances in mining user behavior patterns in online social networks, focusing on the integration of machine learning, deep learning, Natural Language Processing (NLP), and sentiment analysis. The proliferation of social media platforms has led to an explosion in the amount of user-generated content, creating both opportunities and challenges for understanding and predicting user behaviors. Recent advancements in machine learning and deep learning have paved the way for sophisticated techniques to extract and analyze this information, revealing valuable insights into user behavior. A major part of this review discusses how NLP techniques, combined with machine learning algorithms, have been effectively used for sentiment analysis to interpret and gauge user sentiments, fostering an understanding of trends, attitudes, and opinions in social networks. Moreover, we examine how these advanced methods have improved the accuracy of user behavior prediction, enabling more personalized and engaging experiences on these platforms. This comprehensive review not only provides a synthesized understanding of the current state-of-the-art methodologies but also identifies promising directions for future research in the mining of user behavior patterns in online social networks.