def spam_run():
"""Takes a random collection of tweets that haven't yet been run through the
spam run number and runs them through the spam detector, 10k at a time, if
there are at least 10k that haven't yet been put through that run."""
docs_not_yet_included_in_this_run = collect.count( { 'spam_rating' : { '$exists' : False } } )
if docs_not_yet_included_in_this_run >= 10000:
tweet_ids = []
tweet_texts = []
for x in range(10000):
if x % 100 == 0: print 'filled %r random tweets into bucket' % x
docs_to_skip = math.floor(random() * docs_not_yet_included_in_this_run/5)
# Grab a single random tweet that doesn't have the spam_random_run1 field:
tweet = collect.find({ 'spam_rating' : { '$exists' : False } } ).limit(1).skip(int(docs_to_skip)).next()
tweet_texts.append(tweet['text'])
tweet_ids.append(tweet['_id'])
db1 = tweetPreprocessor.tweetDatabase(tweet_texts, batch_size = 10000)
db1.identify_spam()
db1.strip_and_lower_spam()
spam_indices = db1.spam_indices
spam_tweets = db1.spam_tweets_stripped_and_lowered
# Use ids to insert spam_rating back into database:
for tweet_index in range(len(tweet_ids)): # tweet_index is the id of the tweet in the python file!
t_id = tweet_ids[tweet_index] # t_id is the id of the tweet in the mongodb!
if tweet_index % 100 == 0: print 'inserted %r tweets BACK into mongo' % tweet_index
if tweet_index in spam_indices:
collect.update({ '_id' : t_id}, {'$set' : { 'spam_rating' : 1, 'tweet_processor_version' : 2}}, False)
else:
collect.update({ '_id' : t_id}, {'$set' : { 'spam_rating' : 0, 'tweet_processor_version' : 2}}, False)
def spam_run():
"""Takes a random collection of tweets that haven't yet been run through the
spam run number and runs them through the spam detector, 10k at a time, if
there are at least 10k that haven't yet been put through that run."""
docs_not_yet_included_in_this_run = collect.count(
{'spam_rating': {
'$exists': False
}})
if docs_not_yet_included_in_this_run >= 10000:
tweet_ids = []
tweet_texts = []
for x in range(10000):
if x % 100 == 0: print 'filled %r random tweets into bucket' % x
docs_to_skip = math.floor(random() *
docs_not_yet_included_in_this_run / 5)
# Grab a single random tweet that doesn't have the spam_random_run1 field:
tweet = collect.find({
'spam_rating': {
'$exists': False
}
}).limit(1).skip(int(docs_to_skip)).next()
tweet_texts.append(tweet['text'])
tweet_ids.append(tweet['_id'])
db1 = tweetPreprocessor.tweetDatabase(tweet_texts, batch_size=10000)
db1.identify_spam()
db1.strip_and_lower_spam()
spam_indices = db1.spam_indices
spam_tweets = db1.spam_tweets_stripped_and_lowered
# Use ids to insert spam_rating back into database:
for tweet_index in range(
len(tweet_ids)
): # tweet_index is the id of the tweet in the python file!
t_id = tweet_ids[
tweet_index] # t_id is the id of the tweet in the mongodb!
if tweet_index % 100 == 0:
print 'inserted %r tweets BACK into mongo' % tweet_index
if tweet_index in spam_indices:
collect.update(
{'_id': t_id},
{'$set': {
'spam_rating': 1,
'tweet_processor_version': 2
}}, False)
else:
collect.update(
{'_id': t_id},
{'$set': {
'spam_rating': 0,
'tweet_processor_version': 2
}}, False)
if collect.count( {'$and' : [{ 'spam_rating_5ktweetbatches' : { '$exists' : False }},
{ 'text' : { '$exists' : True }},
{ 'user' : { '$exists' : True }} ] } ) >= 5000:
found = collect.find( {'$and' : [{ 'spam_rating_5ktweetbatches' : { '$exists' : False }},
{ 'text' : { '$exists' : True }},
{ 'user' : { '$exists' : True }} ] } ).limit(5000)
# Turn them into two lists: one of ids, one of tweet text:
tweet_ids = []
tweet_texts = []
while found.alive == True:
tweet = found.next()
tweet_texts.append(tweet['text'])
tweet_ids.append(tweet['_id'])
db1 = tweetPreprocessor.tweetDatabase(tweet_texts, batch_size = 5000, sensitivity = .28)
db1.identify_spam()
db1.strip_and_lower_spam()
spam_indices = db1.spam_indices
spam_tweets = db1.spam_tweets_stripped_and_lowered
# Use ids to insert spam_rating back into database:
for tweet_index in range(len(tweet_ids)): # tweet_index is the id of the tweet in the python file!
t_id = tweet_ids[tweet_index] # t_id is the id of the tweet in the mongodb!
if tweet_index % 100 == 0: print 'finished %r tweets' % str(tweet_index)
if tweet_index in spam_indices:
collect.update({ '_id' : t_id}, {'$set' : {'spam_rating_5ktweetbatches' : 1, 'tweet_processor_v2_5k_tweet_batches' : 1}}, False)
else:
collect.update({ '_id' : t_id}, {'$set' : {'spam_rating_5ktweetbatches' : 0, 'tweet_processor_v2_5k_tweet_batches' : 1}}, False)
# If there aren't at least 5000 tweets to work with, then break:
from random import random
# Establish mongo info:
client = MongoClient()
db = client.tweets
collect = db.test_collection
found = collect.find()
etsy_ebay = []
counter = 0
while found.alive == True:
counter +=1
if counter % 100 == 0: print counter
tweet = found.next()
tweet_processed = tweetPreprocessor.singleTweet(tweet['text'])
tweet_processed.strip_and_lower()
if 'etsy' in tweet or 'ebay' in tweet_processed.tweet:
etsy_ebay.append(tweet)
etsy_ebay_tweets = [tweet['text'] for tweet in etsy_ebay]
e_e = tweetPreprocessor.tweetDatabase(etsy_ebay_tweets)
e_e.common_twitter_handles.extend(['etsy', 'ebay'])
e_e.identify_spam()
e_e.strip_and_lower_spam()
def remove_non_ascii(text):
return ''.join([i if ord(i) < 128 else ' ' for i in text])
with open('etsy_ebay_spam.txt', 'w') as outfile:
for x in e_e.spam_tweets_stripped_and_lowered:
outfile.write(remove_non_ascii(x) + '\n')
db = client.tweets
collect = db.random_sample_remote_computer
# Pick up a bunch of tweets, but only if there are at least 10k in there:
for x in range(100):
found = collect.find({'spam_rating': {'$exists': False}}).limit(10000)
# Turn them into two lists: one of ids, one of tweet text:
tweet_ids = []
tweet_texts = []
while found.alive == True:
tweet = found.next()
tweet_texts.append(tweet['text'])
tweet_ids.append(tweet['_id'])
db1 = tweetPreprocessor.tweetDatabase(tweet_texts, batch_size=10000)
db1.identify_spam()
db1.strip_and_lower_spam()
spam_indices = db1.spam_indices
spam_tweets = db1.spam_tweets_stripped_and_lowered
# Use ids to insert spam_rating back into database:
for tweet_index in range(
len(tweet_ids
)): # tweet_index is the id of the tweet in the python file!
t_id = tweet_ids[
tweet_index] # t_id is the id of the tweet in the mongodb!
if tweet_index % 100 == 0: print tweet_index
if tweet_index in spam_indices:
collect.update(
{'_id': t_id},
client = MongoClient()
db = client.tweets
collect = db.sample_from_2009
# Pick up a bunch of tweets, but only if there are at least 5000 in there:
while True:
found = collect.find({'spam_rating' : {'$exists' : False}}).limit(10000)
# Turn them into two lists: one of ids, one of tweet text:
tweet_ids = []
tweet_texts = []
while found.alive == True:
tweet = found.next()
tweet_texts.append(tweet['text'])
tweet_ids.append(tweet['_id'])
db1 = tweetPreprocessor.tweetDatabase(tweet_texts, batch_size = 10000)
db1.identify_spam()
db1.strip_and_lower_spam()
spam_indices = db1.spam_indices
spam_tweets = db1.spam_tweets_stripped_and_lowered
# Use ids to insert spam_rating back into database:
for tweet_index in range(len(tweet_ids)): # tweet_index is the id of the tweet in the python file!
t_id = tweet_ids[tweet_index] # t_id is the id of the tweet in the mongodb!
if tweet_index % 100 == 0: print 'finished %r tweets' % str(tweet_index)
if tweet_index in spam_indices:
collect.update({ '_id' : t_id}, {'$set' : {'spam_rating' : 1, 'tweet_processor_version' : 2}}, False)
else:
collect.update({ '_id' : t_id}, {'$set' : {'spam_rating' : 0, 'tweet_processor_version' : 2}}, False)
}).limit(10000)
print 'Working on the word "%r"' % target_word
target_word_data[target_word] = []
while found.alive == True:
target_word_data[target_word].append(found.next()['text'])
def remove_non_ascii(text):
return ''.join([i if ord(i) < 128 else ' ' for i in text])
path = '/Users/ilya/Projects/twitter_spam/target_word_spam_data/'
spam_tweets = {}
for target_word in target_words:
tweetsdb = tweetPreprocessor.tweetDatabase(target_word_data[target_word])
tweetsdb.identify_spam()
tweetsdb.strip_and_lower_spam()
spam_tweets[target_word] = tweetsdb.spam_tweets_stripped_and_lowered
with open(path + 'v2_raw_spam_' + str(target_word), 'w') as outfile:
for t in spam_tweets[target_word]:
outfile.write(remove_non_ascii(t) + '\n')
spam_percent = {}
for target_word in target_words:
spam_percent[target_word] = float(len(spam_tweets[target_word])) / len(
target_word_data[target_word])
# Write spam percents to a masterfile:
with open('v2_target_word_spam_summary.txt', 'a') as outfile:
for k, v in spam_percent.items():
print "NOT ENOUGH TWEETS THAT INCLUDE THE WORD %s, SKIPPING" % target_word
del target_words[target_words.index(target_word)]
continue
found = db.test_collection.find( {'$text' : { '$search' : target_word}}).limit(10000)
print 'Working on the word "%r"' % target_word
target_word_data[target_word] = []
while found.alive == True:
target_word_data[target_word].append(found.next()['text'])
def remove_non_ascii(text):
return ''.join([i if ord(i) < 128 else ' ' for i in text])
path = '/Users/ilya/Projects/twitter_spam/target_word_spam_data/'
spam_tweets = {}
for target_word in target_words:
tweetsdb = tweetPreprocessor.tweetDatabase(target_word_data[target_word])
tweetsdb.identify_spam()
tweetsdb.strip_and_lower_spam()
spam_tweets[target_word] = tweetsdb.spam_tweets_stripped_and_lowered
with open(path + 'v2_raw_spam_' + str(target_word), 'w') as outfile:
for t in spam_tweets[target_word]:
outfile.write(remove_non_ascii(t) + '\n')
spam_percent = {}
for target_word in target_words:
spam_percent[target_word] = float(len(spam_tweets[target_word]))/len(target_word_data[target_word])
# Write spam percents to a masterfile:
with open('v2_target_word_spam_summary.txt', 'a') as outfile:
for k, v in spam_percent.items():
outfile.write(k + ',' + str(v) + '\n')
