Not sure how accurate this is, but I really like this. Seems to be kind of the way I would expect it to be. Can you guys share what data went into drawing these conclusions?
We monitor a continuous online twitter stream of 10 million tweets a day and do Named Entity Extraction with best of NLP algorithms and models. Our info-graphics are aggregate over months of trending on Frrole.
Just 5 countries, the title and the map was promising more.
On the method: what is the ratio of tweet that are unclassified? I do not believe your data to be accurate on the whole picture. For example, tweet on good restaurant is very frequents in Asia, but restaurant's names will hardly match some keyword.
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[ 2.9 ms ] story [ 40.3 ms ] threadOn the method: what is the ratio of tweet that are unclassified? I do not believe your data to be accurate on the whole picture. For example, tweet on good restaurant is very frequents in Asia, but restaurant's names will hardly match some keyword.