DataMining et Analytics
Posted November 20, 2009 in How-to, twitter | 24 Comments so far Last weekend I was looking for ways to extract Twitter search data in a structured, easily manageable format. The two APIs I was using (Twitter Search and Backtweets) were giving good results – but as a non-developer I couldn’t do much with the raw data they returned. Instead, I needed to get the data into a format like CSV or XLS. Using Google Spreadsheets to extract Twitter data » brelson.com
mini-lien : http://v-ar.ch/er Ingénieur Recherche & Développement à Gamned (Marseille Vieux Port) Thèse soutenue le 24 septembre 2009 [détails / transparents de la soutenance] « Graphes linguistiques multiniveau pour l'extraction de connaissances : l'exemple des collocations », Université Joseph Fourier (Grenoble-I). Centres d'intérêt : Traitement automatique des langues - Extraction d'information - Représentation de l'information par les graphes. Vincent Archer - Recherche & Développement
After recently discovering the excellent methods section on mappingonlinepublics.net, I decided it was time to document my own approach to Twitter data. I’ve been messing around with R and igraph for a while, but it wasn’t until I discovered Gephi that things really moved forward. R/igraph are great for preprocessing the data (not sure how they compare with Awk), but rather cumbersome to work with when it comes to visualization. Last week, I posted a first Gephi visualization of retweeting at the Free Culture Research Conference and since then I’ve experimented some more (see here and here). #FCRC was a test case for a larger study that examines how academics use Twitter at conferences, which is part of what we’re doing at the junior researchers group Science and the Internet at the University of Düsseldorf (sorry, website is currently in German only). Here’s a step-by-step description of how those graphs were created. Generating graphs of retweets and @-messages on Twitter using R and Gephi
Visualisation graphique de l'information par Pierre Nobis (2008) - Partie 2 - Cartographier ses idées
About GUESS GUESS is an exploratory data analysis and visualization tool for graphs and networks. The system contains a domain-specific embedded language called Gython (an extension of Python, or more specifically Jython) which supports the operators and syntactic sugar necessary for working on graph structures in an intuitive manner. An interactive interpreter binds the text that you type in the interpreter to the objects being visualized for more useful integration. GUESS also offers a visualization front end that supports the export of static images and dynamic movies.
Using Netvizz & Gephi to Analyze a Facebook Network « sociomantic labs | Real-time bidding solutions for eCommerce
Applications Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks. Social Network Analysis: easy creation of social data connectors to map community organizations and small-world networks. Biological Network analysis: representing patterns of biological data.