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Hyundai brand has been going through a difficult time recently. A series of failures, breakdowns and problems with Hyundai Rotem high-speed trains connecting Ukrainian cities has triggered a wave of criticism and well-grounded discontent from both passengers, and all other citizens.

In such critical situations negative attitude to a product is sometimes extended to the entire brand. Therefore we wanted to test a hypothesis and check if negative attitude towards Hyundai cars increased due to a rise in popular indignation related to Hyundai Rotem trains.

We have analyzed a media profile in printed press and online media for seven key Ukrainian parties: Party of Regions, All-Ukrainian Union Fatherland (United Opposition), Ukrainian Democratic Alliance for Reform Vitaliy Klychko, Communist Party of Ukraine, Party Ukraine – Forward!, People's Union "Our Ukraine", All-Ukrainian Union "Freedom". The research period is 1 August – 10 October 2012. The most frequently mentioned one in printed press is the Party of Regions with 2024 publications. The second place is taken by the All-Ukrainian Union Fatherland (United Opposition), which was mentioned in 959 articles. In aggregate, the number of mentions of the opposition parties is 3216.

The leader in online news by the number of mentions with a significant margin is also the Party of Regions with 44,042 articles, with its share of the media presence in the general data (no duplicates) on 7 parties being 45%. The total volume of mentions of the opposition parties including the Communist Party of Ukraine is 55% of the general data or 53,692 publications without duplicates.

All details can be found in our research.

If you have ever worked in our system, you could notice that a right side of the SemanticForce dashboard had a plethora of filters: geography, author, source etc.

Today we are discussing a geographic filter: how it works, what it can do, and how you can use it.

Geographic identification offered by SemanticForce makes use of different principles for various types of media:

  • a permanently updated vocabulary of web sites for mass media and regional forums;
  • a profile vocabulary for social webs and blogs (for instance, the systems stores over 10 different spellings for the city of Saint Petersburg, which can be used by its inhabitants in their personal profiles);
  • geographic data in Twitter.