Due to popular demand, the product development team at Texifter is proud to announce that “TopMeta” is now exportable! What does this mean, you might ask? What is TopMeta? When you import either your own data or live social media feeds into DiscoverText, that data often includes various “metadata,” providing a wealth of revealing information about the Tweet, Facebook post, public comment, or survey response you will be analyzing. “TopMeta Explorer” is the function in DiscoverText that allows you to view the number of most (or least) frequently occurring metadata items and filter your data according to that metadata. Considering the wealth of metadata that may be within your data, the ability to easily organize and filter such metadata may turn out to be the difference between substantive and inadequate research. Metadata is Power When might the organization of metadata come in handy, you may also ask? It’s easy to imagine the answer to this question when you consider the kinds of metadata you may collect from live feeds such as the public Twitter API or the GNIP PowerTrack. From those feeds alone, you may collect any of the following metadata (depending on your search method): 1) The time & date of a Tweet, 2) the account name of the tweet’s sender, 3) the real name of the tweet’s sender, 4) the “hashtags” in a tweet, 5) the account name(s) “mentioned” in a tweet, 6) the shortened URL in a tweet, 7) the expanded URL in a tweet, 8) a link to the tweet itself, 9) a direct link to the media in a tweet, 10) the geo-coordinates from which a tweet is sent, 11) the number of “followers” of a tweet’s sender, 12) the number of those “following” a tweet’s sender, 13) the date that a tweet sender’s account was created, 14) the city of the tweet sender, and 15) the “Klout” score of the tweet’s sender. Exporting TopMeta Until now, the “TopMeta Explorer” function has allowed users to easily sort this kind of metadata within DiscoverText. As of this week, this metadata can now be exported as a .CSV file, empowering Enterprise DiscoverText users to more seamlessly utilize the capabilities of DiscoverText, in tandem with their other research tools. We’ll continue to keep you posted about exciting new developments in DiscoverText as they are launched. If you are interested in trying DiscoverText for yourself, sign-up at discovertext.com and email me at email@example.com. I’ll be happy to get you started.
DiscoverText is rolling-out an addition to its analytical toolkit: random sampling. The Web-service already offers an array of tools for text analytics and rigorous, team-based qualitative data analysis. These functions include the ability to code and annotate text, measure inter-rater reliability, adjudicate coder validity, attach memos to text, cluster duplicate and near-duplicate documents, share documents, and to classify text using an active-learning Naive-Bayesian classifier. While still in beta, random sampling is a key new addition. After DiscoverText users amass extraordinary amounts of social media data (for example via the Public Twitter API, the GNIP Powertrack, or the Facebook Social Graph), they can now more easily extract a random sample for analysis. The size of the sample is decided by the user in order to accommodate to iteration, experimentation and other scientific methods. The option is streamlined into the dataset creation process. On the new dataset creation page, you see a sample size prompt. This additional method for data prep and analysis augments current information retrieval techniques, such as search with advanced filtering. It also builds up our framework for expanding available NLP methods from straightforward Bayesian classification, which aims to analyze substantial quantities of data in their original bulk-form, to a menu of computationally intensive methods that can iterate more quickly and effectively against random data samples. For example, the LDA topic model tool we are releasing will be faster and more effective against smaller random samples. This new feature accommodates both an additional analytical approach as well as the opportunity to easily compare results between competing (or complimentary) analytic methods. We look forward to experimenting with this new tool and hearing about how random sampling will enhance the research of our users and users to come. Special Note to DT Users: We need to turn this feature on one account at a time while we are testing it. Drop us a line if you want to try the tool. We’ll keep you posted on the launch as more dataset modifications are pushed live. As always, if you have any questions, feel free to email us anytime at firstname.lastname@example.org. Your feedback is crucial. Sign up and try it out for yourself at discovertext.com.