The growth of social media platforms has created a vast amount of user-generated text data. This data provides important insights into public opinions, feelings, and attitudes. Sentiment analysis uses machine learning techniques to automatically classify and understand these sentiments. This is useful in areas like marketing, politics, public health, and crisis management. This review explores recent advances in machine learning-based sentiment analysis on social media, covering key algorithms, datasets, preprocessing methods, challenges, and future research areas. It highlights the evolving role of deep learning models and hybrid approaches to tackle the complexities of informal and noisy social media text.
Sentiment Analysis, Social Media Analytics, Machine Learning, Opinion Mining, Natural Language Processing (NLP), Big Data, Artificial Intelligence (AI), Real-Time Sentiment Detection.