Think outside the box – No.4

Think outside the box

Innovation needs free thought – and a look beyond the own borders every once in a while. This monthly section provides findings from outside our little box touching on topics connected to Multisensor.

01. With its new photo-service, Google created quite the buzz around the web. (Almost) unlimited storage, free of charge is just one killer feature. What’s even more interesting is the Machine Learning in the background to power the face/content-recognition and the automated sorting algorithms. | read more here
02. Emojis are fun and great to use in text messages. But do you always understand what they mean? Using Machine Learning and natural language processing techniques, Instagram is planning to decode it all, using the vast amount of data inside its network. | read more here
03. What are the advantages of Social-Media-Monitoring? What do you do with all the data you collect? Ruby Rusine from Social Success explains the basics and tools and lists some good tips for futher analysis. | read more here
04. With people creating their own lingo on the web (or just making mistakes while typing on a mobile device) automated analysis of texts are very difficult. There is research trying to compensate this, e.g. to perform sentiment-analysis on informal opinion text, like commerce product ratings. | read more here
05. Verifying online content always deals with finding the “Who, Where and When” of an item. Henk van Ess is an expert in this field and gives some great tips on how to do this using some simple tools. | read more here
06. Some schools in the U.S. have started to use media monitoring tools to keep an eye on their students; “It’s all for protection, to prevent cyberbullying, crime and suicide on schoolgrounds,” they say. But is it really that simple? | read more here
07. With social media carrying so many news to the people today, a group of researchers used an automated media monitoring algorithm to detect breaking news. The result is complicated – to say the least. | read more here