Pretty unrelated to music, but regularly mining text from hundreds of millions of pages on the web can always show up some interesting stats. We decided to run a text entity extraction to find out the most popular first names in a sample of ¼ of a billion pages.
Below are the results for the top 10:
Over 100,000 unique first names were detected in total
Today we are going to introduce to you another piece of technology we have developed at Musicmetric. As you may know, parts of our product are driven by semantic analysis; we don’t just tell you how many people are talking about your artists, but also their opinions, the sentiment and common topics surrounding them. How do we do this? Sentiment analysis is a challenging problem that still has not been solved completely. Many so-called sentiment analysis systems use a very naive method to detect sentiment in a context, i.e. using key words or very basic sentence decomposition. However, human language is not that simple, so these approaches fail to capture irony, sarcasm, slang and other idiomatic expressions.
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