In the digital age, social media platforms generate vast amounts of unstructured data that serve as a goldmine for businesses, marketers, and content creators. Identifying trending topics and understanding content engagement dynamics is critical for strategic decision- making. This report reviews 30 research papers focusing on social media analytics, ranging from big data architecture to advanced deep learning models. Based on this review, we propose a 'Social Media Analyzer' system designed to extract trending hashtags, perform sentiment analysis on user engagement, and provide actionable insights. We select \\\"A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages\\\" (Paper #13) as our base paper for its robust handling of informal text. The proposed work integrates Topic Modelling (LDA) with a Hybrid Deep Learning Classifier to predict content virality and audience sentiment.