Mining Social Data to Extract Intellectual Knowledge
This work addresses the need for structured knowledge extraction from social media for applications such as behavior prediction and decision making, but it appears incremental as it applies existing data mining methods to social data without claiming major breakthroughs.
The paper tackles the problem of extracting intellectual knowledge from social data, specifically using Facebook as a data source, and presents a systematic data mining architecture to analyze this knowledge for applications like human behavior prediction and decision making.
Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.