Editing Community Data Science Workshops (Fall 2015)/Day 2 projects/Twitter
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# Alter code example 3 (twitter3.py) to produce a list of 1000 tweets about a topic. | # Alter code example 3 (twitter3.py) to produce a list of 1000 tweets about a topic. | ||
# Look at those tweets. How does twitter interpret a two word query like "data science" | # Look at those tweets. How does twitter interpret a two word query like "data science" | ||
# Eliminate retweets [hint: look at the tweet object! | # Eliminate retweets [hint: look at the tweet object!] | ||
# For each tweet original tweet, list the number of times you see it retweeted. | # For each tweet original tweet, list the number of times you see it retweeted. | ||
# Get a list of the URLs that are associated with your topic. | # Get a list of the URLs that are associated with your topic. | ||
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'''Geolocation''' | '''Geolocation''' | ||
# Alter the streaming algorithm to include a "locations" filter. You need to use the order sw_lng, sw_lat, ne_lng, ne_lat for the four coordinates. | # Alter the streaming algorithm to include a "locations" filter. You need to use the order sw_lng, sw_lat, ne_lng, ne_lat for the four coordinates. | ||
# What are people tweeting about in Times Square today? (Bonus points: set up a bounding box around TS and around NYC as a whole.) | # What are people tweeting about in Times Square today? (Bonus points: set up a bounding box around TS and around NYC as a whole.) | ||
# Can you find words that are more likely to appear in TS? | # Can you find words that are more likely to appear in TS? |