#- result_count = 0 last_id = None while result_count < num_results : #- # perform a search based on latitude and longitude # twitter API docs: #- import time query = api. # to search for more, we will break our search up into 10 "pages", each # of which will include 100 matching tweets. writer ( csvfile ) #- # add headings to our CSV file #- #- # the twitter API only allows us to query up to 100 tweets at a time. API ( auth ) #- # open a file to write (mode "w"), and create a CSV writer object #- with open ( outfile, "w+" ) as csvfile : csvwriter = csv.
set_access_token ( ACCESS_KEY, ACCESS_SECRET ) print ( last_id ) api = tweepy. OAuthHandler ( CONSUMER_KEY, CONSUMER_SECRET ) auth. #- import sys import csv latitude = 52.3740300 # geographical centre of search longitude = 4.8896900 # geographical centre of search max_range = 10 # search range in kilometres num_results = 1000 # minimum results to obtain outfile = "output.csv" import tweepy # enter twitter info from CONSUMER_KEY = 'mw9ElLqk5F3kEaTta882lAIuP' CONSUMER_SECRET = 'AMF4ZRjmese7rRYj2NbBaA4mEGUF95Y4r0tZ89mCmj4m7IcC5X' ACCESS_KEY = '824274390800011266-saD1Us4iSO5bJeJqjOwt1ibOIABvTUd' ACCESS_SECRET = 'OGEH51sX8Ak0IKENXxRDbBmgqeOuzedBcgUeZaZ0E0Mqh' # setup authorization auth = tweepy. #!/usr/bin/python #- # twitter-search-geo # - performs a search for tweets close to New Cross, and outputs # them to a CSV file.
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