Integrating Social Media Review Clustering and Cat Swarm Algorithm for Market Trend Estimation: A Comparative Study
Keywords:
Social Media Analytics, Market Trend Estimation, Clustering Techniques, Cat Swarm Algorithm, Consumer Sentiment, Data Mining, Optimization Algorithms, User-Generated Content, Business Intelligence, Comparative Study.Abstract
In the digital era, the explosion of user-generated content on social media platforms presents both opportunities and challenges for businesses seeking to understand market trends and consumer behavior. This research paper investigates the integration of clustering techniques applied to social media reviews with the Cat Swarm Algorithm (CSA) to enhance market trend estimation. By utilizing social media data, we aim to extract valuable insights that can drive strategic decision-making in various industries. Our comparative analysis evaluates the performance of the CSA against traditional clustering algorithms, focusing on metrics such as accuracy, convergence speed, and execution time. The results indicate that the CSA not only improves clustering effectiveness but also provides deeper insights into consumer sentiment. This study contributes to the field of market analytics by proposing a novel framework that combines data-driven approaches with innovative algorithmic solutions, ultimately aiming to equip businesses with the tools necessary for navigating the complexities of the modern marketplace.
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