def extract_and_store_tweets(csvfile, nlp, minetweet):
print
print "Start processing %s ..." % csvfile
print "*" * 20
start = time() # measure time
# LOGGING
tweets_count = 0
mentions_count = 0
urls_count = 0
hashtags_count = 0
tags_count = 0
unvalid_tweets = 0
i = 1 # iteroator to remember row number on csv
with open(csvfile, 'r') as f:
# print 'Processing data...'
next(f) # skip csv header
data = csv.reader(f)
# one row at a time
for row in data:
# create Tweet object
t = Tweet()
# Populate Tweet
t.mid = row[0]
t.retweetFromPostId = row[1]
t.userId = row[2]
t.retweetFromUserId = row[3]
t.source = row[4]
t.hasImage = row[5]
t.txt = row[6]
t.geo = row[7]
t.created_at = row[8]
t.deleted_last_seen = row[9]
t.permission_denied = row[10]
# Extract tweet entities
mentions, urls, hashtags, clean = minetweet.extract_tweet_entities(
t.txt)
# add to Tweet
t.mentions = mentions
t.urls = urls
t.hashtags = hashtags
clean = clean # text-only version of the tweet for NLP
# Extract keywords
dico = nlp.extract_dictionary(clean)
# remove stopwords and store clean dico
t.dico = nlp.remove_stopwords(dico)
# extract entities
# TODO : ignore stopwords
# t.entities=nlp.extract_named_entities_from_dico(t.dico)
# Some count for stats
mentions_count += len(mentions)
urls_count += len(urls)
hashtags_count += len(hashtags)
tags_count += len(t.entities)
t.row = i
valid_utf8 = True
try:
t.txt.decode('utf-8')
except UnicodeDecodeError:
unvalid_tweets += 1
valid_utf8 = False
print ' bad encoding : tweet ', t.mid
# pprint(t)
if valid_utf8 is True:
try:
t.save()
tweets_count += 1
except bson.errors.InvalidStringData:
print ' bad encoding : tweet ', t.mid
# pprint(t)
# LOG
print
print "-" * 10
print " mentions_count : %d " % mentions_count
print " urls_count : %d " % urls_count
print " hashtags_count : %d " % hashtags_count
print " unvalid tweets : %d " % unvalid_tweets
print " TOTAL tweet entities : %d " % (mentions_count + urls_count +
hashtags_count)
print " TOTAL named entities (NER): %d " % tags_count
print
print "-" * 10
print "TOTAL tweets processed : %d" % tweets_count
print " done in %.3fs" % (time() - start)
print
def extract_and_store_tweets(csvfile,nlp,minetweet):
print
print "Start processing %s ..."%csvfile
print "*"*20
start=time() # measure time
# LOGGING
tweets_count=0
mentions_count=0
urls_count=0
hashtags_count=0
tags_count=0
unvalid_tweets=0
i=1 # iteroator to remember row number on csv
with open(csvfile, 'r') as f:
# print 'Processing data...'
next(f) # skip csv header
data = csv.reader(f)
# one row at a time
for row in data:
# create Tweet object
t=Tweet()
# Populate Tweet
t.mid=row[0]
t.retweetFromPostId=row[1]
t.userId=row[2]
t.retweetFromUserId=row[3]
t.source=row[4]
t.hasImage=row[5]
t.txt=row[6]
t.geo=row[7]
t.created_at=row[8]
t.deleted_last_seen=row[9]
t.permission_denied=row[10]
# Extract tweet entities
mentions,urls,hashtags,clean=minetweet.extract_tweet_entities(t.txt)
# add to Tweet
t.mentions=mentions
t.urls=urls
t.hashtags=hashtags
clean=clean # text-only version of the tweet for NLP
# Extract keywords
dico=nlp.extract_dictionary(clean)
# remove stopwords and store clean dico
t.dico=nlp.remove_stopwords(dico)
# extract entities
# TODO : ignore stopwords
# t.entities=nlp.extract_named_entities_from_dico(t.dico)
# Some count for stats
mentions_count+=len(mentions)
urls_count+=len(urls)
hashtags_count+=len(hashtags)
tags_count+=len(t.entities)
t.row=i
valid_utf8 = True
try:
t.txt.decode('utf-8')
except UnicodeDecodeError:
unvalid_tweets+=1
valid_utf8 = False
print ' bad encoding : tweet ',t.mid
# pprint(t)
if valid_utf8 is True:
try:
t.save()
tweets_count+=1
except bson.errors.InvalidStringData:
print ' bad encoding : tweet ',t.mid
# pprint(t)
# LOG
print
print "-"*10
print " mentions_count : %d "%mentions_count
print " urls_count : %d "%urls_count
print " hashtags_count : %d "%hashtags_count
print " unvalid tweets : %d "%unvalid_tweets
print " TOTAL tweet entities : %d "%(mentions_count+urls_count+hashtags_count)
print " TOTAL named entities (NER): %d "%tags_count
print
print "-"*10
print "TOTAL tweets processed : %d"%tweets_count
print " done in %.3fs"%(time()-start)
print
def save_tweet(obj):
"""
Saves each tweet to the database
:param obj:
"""
tweet_id = obj['id_str']
if not tweet_id in tweets_map:
# dive into recursion until we hit the original tweet
parent_tweet = None
if 'retweeted_status' in obj and obj['retweeted_status'] is not None:
parent_tweet = save_tweet(obj['retweeted_status'])
location = None
if obj['coordinates'] and obj['coordinates']['coordinates']:
location = WKTElement(
f"POINT({obj['coordinates']['coordinates'][0]} {obj['coordinates']['coordinates'][1]})",
srid=4326)
tweet = Tweet(id=obj['id_str'],
content=obj['full_text'],
location=location,
retweet_count=obj['retweet_count'],
favorite_count=obj['favorite_count'],
happened_at=obj['created_at'])
tweets_map[tweet_id] = True
# if user is present in tweet
if obj['user'] is not None:
# if user is not previously added in hashmap of accounts create new user
user_id = obj['user']['id']
if not user_id in accounts_map:
account = Account(
id=obj['user']['id'],
screen_name=obj['user']['screen_name'],
name=obj['user']['name'],
description=obj['user']['description'],
followers_count=obj['user']['followers_count'],
friends_count=obj['user']['friends_count'],
statuses_count=obj['user']['statuses_count'])
accounts_map[account.id] = SavedAccountType.FULL
else:
# find user in database
account = session.query(Account).filter(
Account.id == user_id).scalar()
# user was previously saved as user_mention and needs to be updated with new attributes which are not present in user_mentions
if accounts_map[user_id] == SavedAccountType.MENTION:
account.update(obj['user'])
accounts_map[account.id] = SavedAccountType.FULL
# add user as an author of the tweet
tweet.author = account
# user mentions
if (obj['entities'] is not None
and obj['entities']['user_mentions'] is not None
and len(obj['entities']['user_mentions'])):
mentions = []
# map all mentions
for mentioned_user in obj['entities']['user_mentions']:
user_id = mentioned_user['id']
# if user is mentioned in the status multiple times (not saved to the db yet, already in accounts hashmap)
# or the user mentions himself before being saved to db
# (they're saved at the end of save_tweet function along the tweet itself)
if (user_id in map(lambda x: x.id, mentions)
or user_id == tweet.author.id):
continue
# check whether the mention wasn't previously saved in hashmap
if not user_id in accounts_map:
account = Account(
id=mentioned_user['id'],
screen_name=mentioned_user['screen_name'],
name=mentioned_user['user']['name'],
)
accounts_map[user_id] = SavedAccountType.MENTION
else:
# find user in database
account = session.query(Account).filter(
Account.id == user_id).scalar()
# append to the array of mentions
mentions.append(account)
# associate hashtags array with tweet
tweet.mentions = mentions
# if place is present in tweet and has all fields present
if (obj['place'] is not None and obj['place']['country_code']
and obj['place']['country']):
# if place is not previously added in hashmap of countries create a new country
country_code = obj['place']['country_code']
if not country_code in countries_map:
country = Country(code=obj['place']['country_code'],
name=obj['place']['country'])
countries_map[country.code] = True
else:
# find country in database
country = session.query(Country).filter(
Country.code == country_code).scalar()
# add place as an country of the tweet
tweet.country = country
if (obj['entities'] is not None
and obj['entities']['hashtags'] is not None
and len(obj['entities']['hashtags'])):
hashtags = []
# map all hashtags
for hashtag_obj in obj['entities']['hashtags']:
# check whether the hashtag wasn't previously saved
hashtag_id = hashtag_obj['text']
# hashtag_id in hashtags of current tweet, not saved to the db yet, already in hashtag hashmap
if hashtag_id in map(lambda x: x.value, hashtags):
continue
if not hashtag_id in hashtags_map:
hashtags_map[hashtag_id] = True
hashtag = Hashtag(hashtag_obj['text'])
else:
# find hashtag in database
hashtag = session.query(Hashtag).filter(
Hashtag.value == hashtag_id).scalar()
# append to the array of hashtags
hashtags.append(hashtag)
# associate hashtags array with tweet
tweet.hashtags = hashtags
# set the parent tweet from the recursion
if parent_tweet:
tweet.parent = parent_tweet
# save tweet object into the db
session.add(tweet)
return tweet
