The exponential growth of social media platforms has generated massive amounts of unstructured text data, offering valuable insights into public opinion, trends, and behaviors. Text mining, when integrated with big data analytics, provides a powerful approach to process, analyze, and extract meaningful information from social media comments. This review paper focuses on various text mining techniques—such as sentiment analysis, topic modeling, and opinion extraction—applied to large-scale social media datasets. The study explores the role of big data frameworks like Hadoop and Spark in handling high-volume, high-velocity data efficiently. Additionally, it highlights preprocessing methods including tokenization, stop-word removal, and feature selection, which are critical for improving model accuracy. The paper also discusses challenges such as data noise, language ambiguity, scalability, and privacy concerns. Overall, this review emphasizes how big data-driven text mining can support decision-making in domains such as marketing, politics, and public health by uncovering patterns and sentiments hidden within social media interactions.
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