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66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
5 years ago
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from __future__ import print_function
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from __future__ import unicode_literals
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from builtins import str, bytes, dict, int
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from builtins import range
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from pattern.web import Twitter, hashtags
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from pattern.db import Datasheet, pprint, pd
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# This example retrieves tweets containing given keywords from Twitter.
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try:
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# We'll store tweets in a Datasheet.
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# A Datasheet is a table of rows and columns that can be exported as a CSV-file.
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# In the first column, we'll store a unique id for each tweet.
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# We only want to add the latest tweets, i.e., those we haven't seen yet.
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# With an index on the first column we can quickly check if an id already exists.
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# The pd() function returns the parent directory of this script + any given path.
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table = Datasheet.load(pd("cool.csv"))
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index = set(table.columns[0])
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except:
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table = Datasheet()
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index = set()
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engine = Twitter(language="en")
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# With Twitter.search(cached=False), a "live" request is sent to Twitter:
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# we get the most recent results instead of those in the local cache.
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# Keeping a local cache can also be useful (e.g., while testing)
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# because a query is instant when it is executed the second time.
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prev = None
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for i in range(2):
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print(i)
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for tweet in engine.search("is cooler than", start=prev, count=25, cached=False):
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print("")
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print(tweet.text)
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print(tweet.author)
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print(tweet.date)
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print(hashtags(tweet.text)) # Keywords in tweets start with a "#".
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print("")
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# Only add the tweet to the table if it doesn't already exists.
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if len(table) == 0 or tweet.id not in index:
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table.append([tweet.id, tweet.text])
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index.add(tweet.id)
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# Continue mining older tweets in next iteration.
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prev = tweet.id
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# Create a .csv in pattern/examples/01-web/
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table.save(pd("cool.csv"))
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print("Total results: %s" % len(table))
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print("")
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# Print all the rows in the table.
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# Since it is stored as a CSV-file it grows comfortably each time the script runs.
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# We can also open the table later on: in other scripts, for further analysis, ...
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pprint(table, truncate=100)
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# Note: you can also search tweets by author:
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# Twitter().search("from:tom_de_smedt")
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