A buzz and e-reputation monitoring tool for twitter based on galois lattices

In the actual interconnected world, the speed of broadcasting of information leads the formation of opinions towards more and more immediacy. Big social networks, by allowing distribution, and therefore broadcasting of information in a almost instantaneous way, also speed up the formation of opinions concerning actuality. Then, these networks are great observatories of opinions and e-reputation. In this e-reputation monitoring task, it is easy to get a set of information (web pages, blog pages, tweets,...) containing a chosen word or a set of words ( a company name, a domain of interest,...), and then we can easily search for the most used words. But a harder, but more interesting task, is to track the set of jointly used words in this dataset, because this latter contains the more shared advice about the initial searched set of words. Precisely, the exhaustive discovering of the shared properties of a collection of objects is the main task of the Galois lattices used in the Formal Concept Analysis. In this article we state clearly the characteristics, advantages and constraints of one of the more successful online social networks: Twitter. Then we detail the difficult task of tracking, on Twitter, the most forwarded information about a chosen subject. We also explain how the characteristics of Galois lattices permit to solve elegantly and efficiently this problem. But, retrieving the most used corpus of words is not enough, we have to show the results in an informative and readable manner, which is not easy when the result is a Galois Lattice. Then we propose a visualisation called topigraphic network of tags, which represent a tag cloud in a network of concepts with a topographic allegory, which permits to visualise the more important concepts found about a given search on Twitter.