Sentiment Analysis in Social Networks with Privacy Preservation: Machine Learning-Based Approaches

Sentiment analysis in social networks has emerged as a powerful tool for understanding user opinions and emotions, attracting significant attention in recent years. This review paper aims to explore the advancements in sentiment analysis techniques, with a particular focus on privacy-preserving methods. We examine various machine learning approaches, including neural networks and text analysis, and their ability to identify and classify sentiments in user-generated content. A critical challenge in this domain is maintaining user privacy while ensuring the accuracy of sentiment detection. The paper reviews current techniques that protect privacy, such as anonymization and encryption, while also enhancing the effectiveness of sentiment analysis. Furthermore, we discuss the implications of deep learning methods for improving the precision of sentiment identification and their potential to comply with ethical and legal privacy requirements. The review concludes with recommendations for future research and the application of sentiment analysis in decision-making and marketing strategies, without compromising user privacy.

Keywords:

Sentiment Analysis , Social Networks , User Opinions and Emotions , Technology , Machine Learning