A FCA-based Framework for Discovering Hidden Knowledge from Twitter Content

Web data mining is a hot research topic that is a technique used to crawl through various web resources to collect required information. As the importance of such web data mining has been recognized, extensive studies have been conducted actively to analyze the data in a Social Networking Service (SNS). In a SNS, a large amount of data, which has a variety of characteristics, is generated through voluntary participation of users, which is also called “big social data”. Big social data can identify not only content registered on the web but also the relations of the friends of users. In this paper, we introduce Formal Concept Analysis (FCA) as the basis for a practical and well founded methodological approach for web data analysis which identifies conceptual structures among data sets. As well as, we propose a framework for discovering hidden knowledge by using polarity from Twitter contents. Additionally, we show the experiments that demonstrate how our framework can be applied for knowledge discovery.