Trends

Identifying trends is one of the greatest things you can get out of Big Data. Learning about trends is valuable in many contexts – to get answers to questions like “In managing my company, should I focus on innovating to succeed in the networked economy or rather ignore this phenomenon?” or “How does the Chinese market perceive electric cars compared to hybrids?” to name only two out of thousands of questions Semantic Visions is able to answer for you.

In our experience, Big Data* from the Internet significantly shapes both customers’ sentiments and public opinion as well as the strategic direction of industrial leaders.

The role of the charts below is to spark your imagination and raise questions on your side. If you conclude that you could benefit from knowing such answers, we will be more than happy to provide them to you. Either on an ad-hoc basis or in the form of Smart Data.

The charts below show the results of Semantic Visions’ Analysis of hundreds of millions of online news articles.

Artificial Intelligence
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)Artificial Intelligence (Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Business Ethics
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)Business Ethics (Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Artificial Intelligence & Industries
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)Artificial Intelligence & Industries (Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Business Ethics & Industries
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)Business Ethics & Industries (Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)

*/ In addition to general-purpose news articles, Semantic Visions collects and analyzes content from websites publishing news on a specific topic, research results, political, business and academic analysis or debate. In general, authors of such articles give facts and detailed information following answers to general questions like who, what, when, where, why and how.

Such logically structured information spread over 3,100 characters per average article analyzed by Semantic Visions provides much more substance for further analysis than logically unstructured tweets with the most popular length ranging from about 70 to 120 characters (source: MIT).

For Semantic Visions, social media is a part of cyberspace from which only relatively limited information can be utilized for the tasks we conduct. Based on our extensive experience, we exploit social media like Twitter or Facebook indirectly and more efficiently by using the collective intelligence of hundreds of thousands of editors and article authors who decide what is important and what is not.

Trends


Identifying trends is one of the greatest things you can get out of Big Data. Learning about trends is valuable in many contexts – to get answers to questions like “In managing my company, should I focus on innovating to succeed in the networked economy or rather ignore this phenomenon?” or “How does the Chinese market perceive electric cars compared to hybrids?” to name only two out of thousands of questions Semantic Visions is able to answer for you.

In our experience, Big Data* from the Internet significantly shapes both customers’ sentiments and public opinion as well as the strategic direction of industrial leaders.

The role of the charts below is to spark your imagination and raise questions on your side. If you conclude that you could benefit from knowing such answers, we will be more than happy to provide them to you. Either on an ad-hoc basis or in the form of Smart Data.

The charts below show the results of Semantic Visions’ Analysis of hundreds of millions of online news articles.

Artificial Intelligence
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Business Ethics
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Artificial Intelligence & Industries
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)
Business Ethics & Industries
(Topic / Semantic Concept), SV Web Volume Index, Monthly (January 2016 - November 2017)

*/ In addition to general-purpose news articles, Semantic Visions collects and analyzes content from websites publishing news on a specific topic, research results, political, business and academic analysis or debate. In general, authors of such articles give facts and detailed information following answers to general questions like who, what, when, where, why and how.

Such logically structured information spread over 3,100 characters per average article analyzed by Semantic Visions provides much more substance for further analysis than logically unstructured tweets with the most popular length ranging from about 70 to 120 characters (source: MIT).

For Semantic Visions, social media is a part of cyberspace from which only relatively limited information can be utilized for the tasks we conduct. Based on our extensive experience, we exploit social media like Twitter or Facebook indirectly and more efficiently by using the collective intelligence of hundreds of thousands of editors and article authors who decide what is important and what is not.

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