From Data to Insight: Leveraging iOLAP and Visualization Tools for Social Media Analysis

Anchita Duggal, Raghu Raja Mehra

Abstract: Data mining on social media involves extracting and analyzing large datasets from platforms to identify patterns and relationships. Tools like Microsoft SharePoint, Sisense, IBM Cognos, Rapid Miner, and Dundas BI use advanced algorithms to visualize networks, where nodes represent users and edges capture interactions. Internet Online Analytical Processing (iOLAP) enables multi - dimensional analysis, combining temporal, social, and semantic data. Techniques like clustering, classification, and association rule mining are used for customer segmentation, behavior prediction, and targeted marketing. Privacy - Preserving Data Mining (PPDM) applies statistical perturbation and cryptographic methods to protect privacy while retaining analytic value. Machine learning algorithms enhance decision - making by processing both structured and unstructured data. OCR and NLP extend data analysis to text and images, while tools like NCapture and APIs for Facebook and Instagram facilitate large - scale data extraction and analysis.

Keywords: Data Mining, User Interaction, Network Visualization, Clustering Techniques, Big Data, Internet Online Analytical Processing (iOLAP), Pattern Recognition