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How to Install and Run WordStat for Stata

How to Install and Run WordStat for Stata

by Adam | April 26, 2018

    WordStat for Stata gives you the capability to analyze any string variables/text data contained in numerical and categorical files that you are analyzing in Stata. WordStat combines natural language processing, content analysis, and statistical techniques to quickly extract topics, patterns, and relationships in large amounts of text. It can process millions of words in seconds and compare extracted themes across any other numerical, categorical or date variables in the Stata file. WordStat for Stata is the same software, with the same...

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Twitter Analysis: Do Tweets set the Political Agenda?

Twitter Analysis: Do Tweets set the Political Agenda?

by Adam | April 25, 2018

Content Analysis and Topic Modeling of Trump/Clinton Tweets These days the world, or at least the media, is all a twitter over the tweets emanating from the President of the United States. It seems as if every morning we wake up to some new missive, observation or attack the President has unleashed against real or perceived foes. More rarely, he arranges 280 or fewer characters to announce or to pronounce on some aspect of government policy. Today, it seems safe to say that these tweets are a major influence on the setting of the daily media agenda....

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Blog: Text Mining Open-Ended Responses to Improve Employee Engagement Survey

Blog: Text Mining Open-Ended Responses to Improve Employee Engagement Survey

by Adam | January 25, 2018

  Engaged employees are better employees. Studies have shown they are more productive, take fewer sick days and have fewer work-related injury or illness.  The U.S. Merit Systems Protection Board (MPSB) has found that departments and programs with more engaged employees perform better. Measuring employee engagement is important so that management can take steps to boost overall employee engagement to prescribed levels or to take actions if it begins to slide. As jobs change, new technology and other factors are introduced into the workplace...

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Text Analytics in Practice: Identifying Dated and Redundant Government Regulations

Text Analytics in Practice: Identifying Dated and Redundant Government Regulations

by Adam | January 17, 2018

  Ok, we acknowledge from the get-go that government regulation isn’t the sexiest topic on the planet. But what if we told you there is a tool/technique that can help us reduce or at least streamline the amount of government regulations we have on the books? Good idea? We think so.  The technique is text analytics and the tools could be software like QDA Miner and WordStat. A team of analysts and data scientists at Deloitte has written the following article “Using Advanced Analytics to Drive Regulatory Reform.” The analysis was done on The Code of...

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Analyzing Online Comments about Refugees with QDA Miner

Analyzing Online Comments about Refugees with QDA Miner

by Alain Forget | January 10, 2018

Immigration, immigrants and refugees are a popular topic around the world these days. It is a topic with wide-ranging points of view and comments are often loaded with emotion. In the book, Representing the Other in European Media Discourse,  Dr. Liisi Laineste, Senior Researcher, Estonian Library Museum, Tartu University contributes Chapter 12, “Othering in Estonian Online Discussions about Refugees.” The chapter is the result of an analysis of the 2015 refugee crisis in Europe in anonymous and non-anonymous Estonian public forums. The...

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Reducing Fraud: Text Analytics in the Insurance Industry

Reducing Fraud: Text Analytics in the Insurance Industry

by Alain Forget | December 15, 2017

Insurance fraud is a worldwide problem. The numbers are staggering. According to the FBI, in the U.S. alone, insurance fraud (ex healthcare) costs Americans $40 billion a year. That works out to $400-to-$700 per family. That means higher premiums. So how do companies crack down on fraud? How can they ferret it out? One of the tools in their tool box is, or should be, text analytics. Insurance companies collect massive volumes of text data every day from customers, agents, adjusters, health care professionals, hospitals, government agencies, police,...

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