I'm implementing an application that tracks the locations of Australia's sharks through analysing a Twitter dataset. So I'm using shark as the keyword and search for the Twitts that contains "shark" and a location phrase.  
So the question is how to identify that "Airlie Beach at Hardy Reef" is the one that is correlated to "shark"? If it's possible, can anyone provide a working code of Python to demonstrate​? Thank you so much!
If you've already used NER to extract a list of locations, could you then create a table of target words and assign probabilities of being the correct location? For example, you are interested in beaches not hospitals. If beach is mentioned within the location, the probability of being the correct location increases. Another hacky way of doing it might be determining the number of characters or tokens between the word shark and the location - hoping that the smaller the distance, the more likely the word is to be related to the actual attack.
Extract the most relevant location corresponding to a keyword