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The all-in-one software for Content Analysis and Text Mining Hello We are pleased to announce the release of T-LAB 8.0. This version represents a major change in the usability and the effectiveness of our software for text analysis. The most significant improvements concern the integration of bottom-up (i.e. unsupervised) methods for exploratory text analysis with top-down (i.e. supervised) approaches for the automated classification of textual units like words, sentences, paragraphs and documents. Among other things, this means that - besides discovering emerging patterns of words and themes from texts - the users can now easily build, apply and validate their models (e.g. dictionaries of categories or pre-existing manual categorizations) both for classical content analysis and for sentiment analysis. For this purpose several T-LAB functionalities have been expanded and a new ergonomic and powerful tool named 'Dictionary-Based Classification' has been added. No specific dictionaries have been built in; however, with some minor re-formatting, lots of resources available over the Internet and customized word lists can be quickly imported. Last but not least, in order to meet the needs of many customers, temporary licenses of the software are now on sale; moreover, without any time limit, the trial mode of the software now allows you to analyse your own texts up to 20 kb in txt format, each of which can include up to 20 short documents. To learn more, use the following link http://www.tlab.it/en/80news.php The Demo, the User's Manual and the Quick Introduction are available at http://www.tlab.it/en/download.php Kind Regards The T-LAB Team web: http://www.tlab.it/ e-mail: info@tlab.it

Using Overview to analyze 4500 pages of documents on security contractors in Iraq by Jonathan Stray on 02/21/2012 0 This post describes how we used a prototype of the Overview software to explore 4,500 pages of incident reports concerning the actions of private security contractors working for the U.S. State Department during the Iraq war. This was the core of the reporting work for our previous post, where we reported the results of that analysis. The promise of a document set like this is that it will give us some idea of the broader picture, beyond the handful of really egregious incidents that have made headlines. To do this, in some way we have to take into account most or all of the documents, not just the small number that might match a particular keyword search. But at one page per minute, eight hours per day, it would take about 10 days for one person to read all of these documents - to say nothing of taking notes or doing any sort of followup. This is exactly the sort of problem that Overview would like to solve. The reporting was a multi-stage process: Splitting the massive PDFs into individual documents and extracting the text Exploration and subject tagging with the Overview prototype Random sampling to estimate the frequency of certain types of events Followup and comparison with other sources

Blog Learning to love…grep (let the computer search text for you) Posted by Michelle Minkoff on Aug 9, 2012 in Blog, Uncategorized | No Comments I've gotten into the habit of posting daily learnings on Twitter, but some things require a more in-depth reminder. I also haven't done as much paying as forward as I'd like (but I'm having a TON of fun! and dealing with health problems! but mostly fun!) I'd like to try to start posting more helpful tips here, partially as a notebook for myself, and partially to help others with similar issues. Today's problem: I needed to search for a few lines of text, which could be contained in any one of nine files with 100,000 lines each. Opening all of the files took a very long time on my computer, not to mention executing a search. Enter the "grep" command in Terminal, that allows you to quickly search files using the power of the computer.

An easy to use method for content analysis

Assessing Language Patterns: A Look at Texas Newspapers, 1829-2008 This visualization plots the language patterns embedded in 232,567 pages of historical Texas newspapers, as they evolved over time and space. For any date range and location, you can browse the most common words (word counts), named entities (people, places, etc), and highly correlated words (topic models). [ About Mapping Texts ]

"ReConstitution 2012, a fun experiment by Sosolimited, processes transcripts from the presidential debates, and recreates them with animated words and charts. Part data visualization, part experimental typography, ReConstitution 2012 is a live web app linked to the US Presidential Debates. During and after the three debates, language used by the candidates generates a live graphical map of the events. Algorithms track the psychological states of Romney and Obama and compare them to past candidates. The app allows the user to get beyond the punditry and discover the hidden meaning in the words chosen by the candidates. As you let the transcript run, numbers followed by their units (like "18 months") flash on the screen, and trigger words for emotions like positivity, negativity, and rage are highlighted yellow, blue, and red, respectively. You can also see the classifications in graph form. There are a handful of less straightforward text classifications for truthy and suicidal, which are based on linguistic studies, which in turn are based on word frequencies. These estimates are more fuzzy. So, as the creators suggest, it's best not to interpret the project as an analytical tool, and more of a fun way to look back at the debate, which it is. It's pretty fun to watch. Here's a short video from Sosolimited for more on how the application works: "

VIDEO: document mining with Overview by Jonathan Stray on 10/31/2012 0 With the release of the new, web-only version of Overview that runs in your browser, we thought it was time to make a little video showing how to use it. If that doesn't answer your questions, see also the help page, and the FAQ.

Document mining shows Paul Ryan relying on the the programs he criticizes by Jonathan Stray on 11/02/2012 0 One of the jobs of a journalist is to check the record. When Congressman Paul Ryan became a vice-presidential candidate, Associated Press reporter Jack Gillum decided to examine the candidate through his own words. Hundreds of Freedom of Information requests and 9,000 pages later, Gillum wrote a story showing that Ryan has asked for money from many of the same Federal programs he has criticized as wasteful, including stimulus money and funding for alternative fuels. This would have been much more difficult without special software for journalism. In this case Gillum relied on two tools: DocumentCloud to upload, OCR, and search the documents, and Overview to automatically sort the documents into topics and visualize the contents. Both projects are previous Knight News Challenge winners. But first Gillum had to get the documents. As a member of Congress, Ryan isn't subject to the Freedom of Information Act. Instead, Gillum went to every federal agency - whose files are covered under FOIA - for copies of letters or emails that might identify Ryan's favored causes, names of any constituents who sought favors, and more. Bit by bit, the documents arrived - on paper. The stack grew over weeks, eventually piling up two feet high on Gillum's desk. Then he scanned the pages and loaded them into the AP's internal installation of DocumentCloud. The software converts the scanned pages to searchable text, but there were still 9000 pages of material. That's where Overview came in. Developed in house at the Associated Press, this open-source visualization tool processes the full text of each document and clusters similar documents together, producing a visualization that graphically shows the contents of the complete document set. "I used Overview to take these 9000 pages of documents, and knowing there was probably going to be a lot of garbage or ext

ELAN description ELAN is a professional tool for the creation of complex annotations on video and audio resources. With ELAN a user can add an unlimited number of annotations to audio and/or video streams. An annotation can be a sentence, word or gloss, a comment, translation or a description of any feature observed in the media. Annotations can be created on multiple layers, called tiers. Tiers can be hierarchically interconnected. An annotation can either be time-aligned to the media or it can refer to other existing annotations. The textual content of annotations is always in Unicode and the transcription is stored in an XML format. ELAN provides several different views on the annotations, each view is connected and synchronized to the media playhead. Up to 4 video files can be associated with an annotation document. Each video can be integrated in the main document window or displayed in its own resizable window. ELAN delegates media playback to an existing media framework, like Windows Media Player, QuickTime or JMF (Java Media Framework). As a result a wide variety of audio and video formats is supported and high performance media playback can be achieved. ELAN is written in the Java programming language and the sources are available for non-commercial use. It runs on Windows, Mac OS X and Linux.

Be sure to view the video tutorial: http://www.cc.gatech.edu/gvu/ii/jigsaw/Jigsaw-tutorial.movhttp://www.cc.gatech.edu/gvu/ii/jigsaw/Jigsaw-tutorial.mov http://www.cc.gatech.edu/gvu/ii/jigsaw/views.html Jigsaw: Visual Analytics for Exploring and Understanding Document Collections System Views Jigsaw presents the individual reports in a document collection and the entities within those reports through a series of visualizations. We call these visualizations the system views. Below, we illustrate each view provided by the system and briefly describe their characteristics. Click on the individual images to see a larger version of the view. Also, a tutorial video illustrates the different views as well and the interactive behavior for each view can be seen on the video tutorial page. -tj

Also see "The Information Interfaces Group, an HCI research group in the School of Interactive Computing at Georgia Tech, develops computing technologies that help people take advantage of information to enrich their lives. " http://www.cc.gatech.edu/gvu/ii/