Group items tagged

Cogito semantic technology The material analyzed through the intelligence process is mostly unstructured: communications, conversations, data, news, etc., and rarely has an organized form, such as a database structure. As a consequence, the activities of analysis and processing can be highly resource consuming, and prone to error. The work of analysts and knowledge workers remains essential, and technology cannot fully replace it (and never will). Nevertheless, with new generation software based on semantic analysis, it is now possible to understand data automatically as well as to support reasoning and stimulate intuition.

The Manchester social networks group (MSNG) is a cross-disciplinary research group located in the School of Social Sciences. MSNG aims to discuss and to promote the application and development of Social Network Analysis methodology to answer important research questions in social science. The following SNA topics are of interest to the group: Data collection for social network analysis Longitudinal networks and network formation Neighbourhood and network variations in social and health outcomes Qualitative network analysis Social Networks and Social Movements Statistical modelling of social networks Visualising and describing social networks

Sentiment Analysis is a terribly difficult problem. The problem is in defining the problem - the input is the problem - but you can't avoid it, IT IS the future, and I'm very optimistic about it! Professor Bing Liu started off the Sentiment Analytics Symposium yesterday with the statement above and I couldn't agree more. Subsequently he gave the pre-workshop audience a detailed 3.5 hour overview of the state of text analytics. It was not surprising to me that almost a quarter of the audience were young developers (MacBook Pro in hand), with the hopes to learn how to incorporate their own sentiment analysis engines into their business applications.

Discourse Network Analyzer (DNA) is a Java-based application for qualitative category-based content analysis. It serves two purposes: coding statements of actors into categories, and converting these structured data into networks that are readable by UCINET, visone and other network-analytic software packages. The software can extract either bipartite (affiliation) networks or adjacency networks. It is complementary to semantic network analysis applications because it neither relies on algorithms for automatic text processing, nor does it focus on the internal meaning or mental representation of a single text or document. Instead, tags are applied to the text data by manual inspection, thus rendering it more flexible, yet at the same time more work-intensive. DNA can be used to code a large body of text documents and then convert them into graphs. The application is currently being developed, tested and heavily used in my dissertation research project on German pension politics. Updates will be posted here as soon as something has been published. If you use DNA, I would love to hear from you about your project (if possible, via the DNA-help mailing list). For more information about the software, please consult the documentation or obtain a free copy from the download page.

Amnesty International USA and the Standby Task Force have launched an ambitious campaign to crowdsource analysis of Syrian satellite imagery for military movements, demonstrations, and checkpoints. So far, volunteers have tagged more than 2,000 potential troublespots. Is DIY intelligence analysis the future of human rights work?

From basic statistics to machine learning and new ways to think about visualization, the Data Science Starter Kit gives you the tools you need to get started with data. If you haven't yet taken the leap, why wait? And if you're already experienced with data, the Starter Kit will push you further. The package includes (8) titles on R, basic statistics and data analysis, Python, machine learning, and visualization. This kit includes everything you need from analysis, visualization, to management.

A number of instructors have been using NodeXL to help teaching social network analysis. It is relatively easy to use compared to many other network analysis and visualization tools, while still providing a rich set of metrics and visualization features. It is also free and integrates with Excel 2007, which many users are familiar with. This page is meant to collect information that can be useful in teaching with NodeXL.

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

Recorded Future offers robust tools for temporal and predictive analysis including advanced visualizations, data for predictive modelling, and fine-grain Future oriented alerts. Customers around the world are using Recorded Future Premium to monitor media on the web, understand news and forecast a variety events, track sentiment surrounding events and more.

The advantage of NodeXL, particularly for graphing Twitter communities, is it has built-in features for grabbing the data for you. Not only that the coding is clever enough to handle the data collection for mere mortals, so when you hit your rate limit NodeXL waits until it should be able to get more data. NodeXL also has "built-in connections for getting networks from Flickr, YouTube, and your local email. Additional importers for Exchange Email, Facebook, and Hyperlink networks are available".

Social Mention is a social media search and analysis platform that aggregates user generated content from across the universe into a single stream of information. It allows you to easily track and measure what people are saying about you, your company, a new product, or any topic across the web's social media landscape in real-time. Social Mention monitors 100+ social media properties directly including: Twitter, Facebook, FriendFeed, YouTube, Digg, Google etc.

Rioting and related intra-crowd dynamics are significant human processes, but we know less about the basic behavioral science and subsequent processes that drive and shape rioting than we would like to. This is due, in large part, to the difficulty in studying riots on the ground and to the sheer complexity of riot phenomena. We know even less about the geographical dynamics of rioting, even though there is a dedicated (but only general) appreciation that geography is important. Existing work has, for the most part, adopted the most straightforward path to discovery, by examining coarse (city-scale) geographies of rioting, or in the few instances where intra-crowd riot dynamics are considered they have focused on stylized abstractions of behavior. Because of the difficulties of using standard social science inquiry to study riots (surveys, ethnographic analysis, interviews), many researchers have turned to computer modeling to create synthetic riots that can be configured, sampled, and experimented with. But, building models of something as bewilderingly complex as rioting is really quite difficult and so many short-cuts are taken. In particular, models are usually cellular-based in form (where rasters represent people and their local environment) and founded on physical interactions between relatively "dumb" particle-people (where continuum mechanics, random walks, or particle-particle forces serve as a substitute for socio-spatial interaction and behavior).

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