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Building a Social Sifter

March 5, 2012 by Stuart Shulman Leave a Comment

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Check out this video introducing our latest experiments creating custom social “sifters” to winnow down a lake of social media data and leave behind only those items that are truly responsive to your search. Great tool, or greatest tool ever? You be the judge.

Filed Under: DiscoverText, research Tagged With: analytics, crowdsourcing, DiscoverText, Machine Classifiers, Machine Learning, SIFTER, Social Media, social sifter

DiscoverText Introduces Tools for Random Sampling

November 1, 2011 by Josh Sowalsky Leave a Comment

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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  help@discovertext.com. Your feedback is crucial. Sign up and try it out for yourself at discovertext.com.

Filed Under: DiscoverText, Facebook, general, GNIP, product, research, Twitter Tagged With: analytics, API Graph, Bayesian, Bayesian classification, classification, Code Text, coding, Data Mining, DiscoverText, Firehose, Gary King, GNIP, Machine Classifiers, Machine Learning, methodology, Natural Language Processing, NLP, PowerTrack, Qualitative Data Analysis, random, Random Sample, Random Sampling, Research, research methodology, sample, Sampling, Social Graph, Social Media, social media monitoring, Texifter, Text Analysis, Text Analytics, twitter, Twitter API

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Bots, Trolls, and Elections

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Oh, Canada? A quick research note: your election is definitely under attack via Twitter, despite assertions to the contrary. This is part one of a nine-part series written in the run-up to the 2019 Canadian election. The research was reported by CTV and CBC. Twitter Data Collection on Canadian Elections For the last month, we […]

108 Authenticated Reviews

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We have been blessed with an engaging group of software users who wrote 108 authenticated DiscoverText reviews. Find out why researchers across the disciplines trust the platform.

Academic Citations

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We have long worked with diverse and talented scholars to support their publication efforts with free open source and affordable commercial software with academic discounts, free trainings, and almost unlimited enthusiasm for the challenge of capturing and coding text data. We are very pleased to announce a new crop of DiscoverText citations. You can see […]

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Boolean defined search, n-grams, word clouds, and custom topic dictionaries

Power tools for text analysis and machine-learning

Discover central topics and also elusive but valuable unexpected or rare concepts. Use this information to train machine-learning classifiers to recognize relevant text and social media data. Jump into data using an interactive word CloudExplorer or build a mini topic dictionary using “defined” search. Try our new CloudExplorer listview for seeing the top 300 bigrams or trigrams in your data. Merge or delete terms as part of building your custom text analytics model.

DiscoverText is powered by uClassify

Machine-learning for everyone

Create gold standard training sets by labeling your training data accurately and reliably using our state-of-the-art collaborative annotation system. Then use our trusted, multilingual machine learning web service (uClassify) to create and apply your own custom-trained text classifiers. Please take the time to check out the work being done by the large and growing uClassify community.

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