To quickly learn the latest about gathering Twitter tweets for research, importing SurveyMonkey data, or using state of the art text analytics tools, check out our most recent Texifter tutorial videos. While they have a bit of a “home brew” flavor, we’ve been told they do help jump start the process of learning about exciting new tools for text.
Texifter Announces Strategic Partnership with SurveyMonkey to Improve Survey Data Analytics
Combining the power and versatility of Texifter’s DiscoverText analytics with the reach of the world’s largest survey website. AMHERST, MA., May 27 2014—Texifter, a developer of social data and text analysis tools, today announced a new strategic partnership with SurveyMonkey, the world’s largest survey website, to provide advanced text analytics capabilities to SurveyMonkey users through its cloud-based platform, DiscoverText. SurveyMonkey is known for intuitive interfaces and communications features that allow researchers to collect millions of survey responses every day. When surveys produce very large numbers of responses to open-ended questions, it can be a challenge to analyze all of the verbatim data. This is especially true for those relying on spreadsheet software as their primary text analytics tool. DiscoverText provides an accessible “point and click” solution for these and other analytic challenges. Starting today, all DiscoverText users will be able to log in to SurveyMonkey to easily import existing survey data. Researchers can use a 30-day free trial to apply the full range of Discover Text’s powerful software tools to both the open ended answers and the structured survey metadata. Texifter’s “five pillars of text analytics” approach combines search, filtering, clustering, human-coding, and machine-learning. Once registered on DiscoverText, newcomers have access to a wide spectrum of online data feeds. Facebook, Tumblr, YouTube, WordPress, Disqus, and Twitter data can be gathered, managed, and analyzed in DiscoverText alongside SurveyMonkey responses, email, and other forms of text data. “The Texifter team is excited to be introducing SurveyMonkey users to the powerful and flexible text analytics tools in DiscoverText,” said founder and CEO Stuart Shulman. “We are confident that once people try out features like clustering and custom machine-learning, they’ll begin to see new possibilities for generating insights from bigger and more diverse collections of unstructured free text.” This strategic partnership signals the latest phase in the evolution of DiscoverText. Originally built for federal agencies sorting large-scale public comment collections, the four-year old collaborative research platform now serves a wide variety of public and private sector clients, as well as the academic research community. Texifter is a spin-out company based on information science research by Dr. Stuart W. Shulman, who directed the development of numerous human language tools for reviewing large numbers of public comments. Texifter Contact Stuart Shulman https://texifter.com email@example.com
Document relevance is a key challenge for social media research. The specific problem of “word sense disambiguation” is widespread. If I am interested in “banks” where money is stored, I want to exclude mentions of river banks. If I am “Delta” airlines, I do not want to see social data about Delta faucets, Delta force, or those pesky river deltas. If I run a sports team like the Pittsburgh Penguins, the massive numbers of Facebook posts and Tweets about flightless but adorable birds are equally problematic. There are very few social media analytics projects that can easily avoid the challenge of sorting relevant and irrelevant documents. At Texifter, we have refined a powerful set of tools and techniques for doing word sense disambiguation. This 5-minute video uses the example of Governor Chris Christie to illustrate how the five pillars of text analytics can help anyone to identify and remove irrelevant documents from an ambiguous social data collection. The principles are very similar to spam filtering in email; we use the same mathematics. Using DiscoverText, we argue an individual or small collaborative team can create a custom machine classifier for the task in just a few hours. Someday, we hope to get this down to a few minutes.
It was a great joy to return to the University of Amsterdam and give this talk to my old friend Richard Rogers and his 100+ attentive workshop attendees.
Just about six hours left to win valuable historical twitter datasets and powerful text analytics software. This is by far our best Facebook raffle yet. To enter:
- Login to Facebook
- Visit this URL: https://bit.ly/1421tWP
- Tweet about the raffle, follow DiscoverText on Twitter, or like on Facebook.
- Do all three to increase your chances.
- Refer friends to do better still.
The winner will get three 10-day historical Twitter datasets, with Power Track search operators enable by our friends @gnip as well as gratis use of the DiscoverText software platform. Runners up will also get valuable software prizes for a full year.