def save_tweet_sentiment_scores_to_csv(sentiment_file):
dbm = DBManager(collection='tweets_esp_hpai')
sa = SentimentAnalyzer()
# get tweets from file
tweets_selected = []
with open(sentiment_file, 'r') as csv_file:
csv_reader = csv.DictReader(csv_file)
for row in csv_reader:
tweet = dbm.find_record({
'sentiment_score':
float(row['score']),
'$or': [{
'text': row['texto']
}, {
'extended_tweet.full_text': row['texto']
}]
})
if tweet:
tweet_text = tweet['extended_tweet']['full_text'] \
if 'extended_tweet' in tweet else tweet['text']
sentiment_score_affin = sa.normalize_score(tweet['sentiment_score_affin']) \
if 'sentiment_score_affin' in tweet else ''
sentiment_score_sentipy = sa.normalize_score(tweet['sentiment_score_sentipy']) \
if 'sentiment_score_sentipy' in tweet else ''
sentiment_polyglot = sa.normalize_score(tweet['sentiment_score_polyglot']) \
if 'sentiment_score_polyglot' in tweet and tweet['sentiment_score_polyglot'] else ''
tweets_selected.append({
'id': tweet['id'],
'texto': tweet_text,
'score_polyglot': sentiment_polyglot,
'score_affin': sentiment_score_affin,
'score_sentipy': sentiment_score_sentipy,
'final_score': tweet['sentiment_score']
})
else:
logging.info('Could not find the tweet {}'.format(
row['texto']))
logging.debug('Recovered {} tweets'.format(len(tweets_selected)))
output_file = '../data/bsc/processing_outputs/sentiment_analysis_sample_scores.csv'
logging.info('Saving tweets to the CSV {}'.format(output_file))
with open(output_file, 'w') as csv_file:
headers = tweets_selected[0].keys()
csv_writer = csv.DictWriter(csv_file, fieldnames=headers)
csv_writer.writeheader()
for tweet_selected in tweets_selected:
csv_writer.writerow(tweet_selected)
def export_sentiment_scores_from_ids(file_tweet_ids, collection, config_fn):
dbm = DBManager(collection=collection, config_fn=config_fn)
tweets_to_export = []
with open(file_tweet_ids, 'r') as csv_file:
csv_reader = csv.DictReader(csv_file)
for row in csv_reader:
tweet_id = row['id']
print('Processing tweet: {}'.format(tweet_id))
tweet_obj = dbm.find_record({'id_str': str(tweet_id)})
if tweet_obj is None:
print('Missing tweet...')
continue
tweet_to_export = {
'id': tweet_id,
'text': tweet_obj['complete_text']
}
sentiment_obj = tweet_obj['sentiment']
if 'sentiment_score_polyglot' in sentiment_obj:
tweet_to_export['score_polyglot'] = \
sentiment_obj['sentiment_score_polyglot']
if 'sentiment_score_sentipy' in sentiment_obj:
tweet_to_export['score_sentipy'] = \
sentiment_obj['sentiment_score_sentipy']
if 'sentiment_score_affin' in sentiment_obj:
tweet_to_export['score_affin'] = \
sentiment_obj['sentiment_score_affin']
if 'sentiment_score_vader' in sentiment_obj:
tweet_to_export['score_vader'] = \
sentiment_obj['sentiment_score_vader']
tweet_to_export['sentiment_score'] = sentiment_obj['score']
tweet_to_export['human_label'] = row['label']
tweets_to_export.append(tweet_to_export)
output_file = '../data/bsc/processing_outputs/sentiment_scores_from_ids.csv'
print('Saving tweets to the CSV {}'.format(output_file))
with open(output_file, 'w') as csv_file:
headers = tweets_to_export[0].keys()
csv_writer = csv.DictWriter(csv_file, fieldnames=headers)
csv_writer.writeheader()
for tweet_to_export in tweets_to_export:
csv_writer.writerow(tweet_to_export)
class NetworkAnalyzer:
__dbm_tweets = None
__dbm_users = None
__dbm_networks = None
__network = None
__graph = None
__nodes = set()
__unknown_users = set()
__node_sizes = None
def __init__(self, colletion=None):
if not None:
self.__dbm_tweets = DBManager(colletion)
self.__dbm_users = DBManager('users')
self.__dbm_networks = DBManager('networks')
self.__network = []
def __computer_ff_ratio(self, friends, followers):
if followers > 0 and friends > 0:
return friends / followers
else:
return 0
# Get interactions in of a given users
def get_in_interactions(self, user_screen_name):
# compute in interactions, meaning, interactions in which the user
# was mentioned, retweeted, quoted, replied
in_inter_query = {
'interactions.' + user_screen_name: {
'$exists': 1
},
'screen_name': {
'$ne': user_screen_name
}
}
n_users = self.__dbm_users.search(in_inter_query)
in_interactions_dict, in_rts, in_rps = {}, {}, {}
in_qts, in_mts = {}, {}
total_in_interactions = 0
total_in_retweets, total_in_replies = 0, 0
total_in_mentions, total_in_quotes = 0, 0
for n_user in n_users:
n_user_interactions = n_user['interactions']
for i_user, interactions in n_user_interactions.items():
if i_user == user_screen_name:
in_interactions_dict[
n_user['screen_name']] = interactions['total']
total_in_interactions += interactions['total']
if 'retweets' in interactions.keys():
total_in_retweets += interactions['retweets']
in_rts[
n_user['screen_name']] = interactions['retweets']
if 'replies' in interactions.keys():
total_in_replies += interactions['replies']
in_rps[n_user['screen_name']] = interactions['replies']
if 'mentions' in interactions.keys():
total_in_mentions += interactions['mentions']
in_mts[
n_user['screen_name']] = interactions['mentions']
if 'quotes' in interactions.keys():
total_in_quotes += interactions['quotes']
in_qts[n_user['screen_name']] = interactions['quotes']
in_interactions_obj = {
'total': {
'count': total_in_interactions,
'details': in_interactions_dict
},
'replies': {
'count': total_in_replies,
'details': in_rps
},
'retweets': {
'count': total_in_retweets,
'details': in_rts
},
'mentions': {
'count': total_in_mentions,
'details': in_mts
},
'quotes': {
'count': total_in_quotes,
'details': in_qts
}
}
user_dict = {'in_interactions': in_interactions_obj}
return user_dict
# Get interactions out of a given users
def get_out_interactions(self, user_screen_name):
user = self.__dbm_users.search({'screen_name': user_screen_name})[0]
# compute out interactions, meaning, interactions originated by
# the user
user_interactions = user['interactions']
out_interactions_dict, out_rts = {}, {}
out_rps, out_qts, out_mts = {}, {}, {}
total_out_interactions, total_out_retweets = 0, 0
total_out_mentions, total_out_replies = 0, 0
total_out_quotes = 0
for recipient, interactions in user_interactions.items():
out_interactions_dict[recipient] = interactions['total']
total_out_interactions += interactions['total']
if 'retweets' in interactions:
total_out_retweets += interactions['retweets']
out_rts[recipient] = interactions['retweets']
if 'replies' in interactions:
total_out_replies += interactions['replies']
out_rps[recipient] = interactions['replies']
if 'mentions' in interactions:
total_out_mentions += interactions['mentions']
out_mts[recipient] = interactions['mentions']
if 'quotes' in interactions:
total_out_quotes += interactions['quotes']
out_qts[recipient] = interactions['quotes']
out_interactions_obj = {
'total': {
'count': total_out_interactions,
'details': out_interactions_dict
},
'replies': {
'count': total_out_replies,
'details': out_rps
},
'retweets': {
'count': total_out_retweets,
'details': out_rts
},
'mentions': {
'count': total_out_mentions,
'details': out_mts
},
'quotes': {
'count': total_out_quotes,
'details': out_qts
}
}
# compile all information in a dictionary
user_dict = {'out_interactions': out_interactions_obj}
return user_dict
def create_users_db(self, clear_collection=False):
logging.info(
'::. Network Analyzer: Creating database of users, it can take several minutes, please wait_'
)
if clear_collection:
self.__dbm_users.clear_collection()
users = self.__dbm_tweets.get_unique_users()
users_count = len(users)
logging.info(
'::. Network Analyzer: Extracted {0} unique users from the database...'
.format(users_count))
progress = 1
for user in users:
db_user = {
'screen_name':
user['screen_name'],
'friends':
user['friends'],
'followers':
user['followers'],
'ff_ratio':
self.__computer_ff_ratio(user['friends'], user['followers']),
'interactions':
user['interactions'],
'tweets':
user['tweets_count'],
'original_tweets':
user['original_count'],
'rts':
user['retweets_count'],
'qts':
user['quotes_count'],
'rps':
user['replies_count'],
'verified':
user['verified']
}
filter_query = {'screen_name': user['screen_name']}
logging.debug(
'::. Network Analyzer: Updating/creating user {0} ({1}/{2})...'
.format(user['screen_name'], progress, users_count))
progress += 1
self.__dbm_users.update_record(filter_query,
db_user,
create_if_doesnt_exist=True)
def generate_network(self,
subnet_query={},
depth=1,
file_name='network',
override_net=False):
net_query = subnet_query.copy()
net_query.update({'depth': depth})
ret_net = self.__dbm_networks.search(net_query)
# the net doesn't exist yet, let's create it
if ret_net.count() == 0 or override_net:
logging.info(
'Generating the network, it can take several minutes, please wait_'
)
users = self.__dbm_users.search(subnet_query)
# for each user generate his/her edges
for user in users:
if 'ff_ratio' in user.keys():
u_ff_ratio = user['ff_ratio']
else:
u_ff_ratio = self.__computer_ff_ratio(
user['friends'], user['followers'])
exists = user['exists'] if 'exists' in user.keys() else ''
self.__nodes.add(
tuple({
'screen_name': user['screen_name'],
'ff_ratio': u_ff_ratio,
'exists': exists
}.items()))
for interacted_user, interactions in user[
'interactions'].items():
iuser = self.__dbm_users.find_record(
{'screen_name': interacted_user})
if not iuser:
if depth > 1:
iuser_ffratio = self.__get_ffratio(interacted_user)
if not iuser_ffratio:
self.__unknown_users.add(interacted_user)
continue
else:
self.__unknown_users.add(interacted_user)
continue
else:
if 'ff_ratio' in iuser.keys():
i_ff_ratio = iuser['ff_ratio']
else:
i_ff_ratio = self.__computer_ff_ratio(
iuser['friends'], iuser['followers'])
exists_iuser = iuser['exists'] if 'exists' in iuser.keys(
) else ''
self.__nodes.add(
tuple({
'screen_name': iuser['screen_name'],
'ff_ratio': i_ff_ratio
}.items()))
edge = {
'nodeA': {
'screen_name': user['screen_name'],
'ff_ratio': u_ff_ratio,
'exists': exists
},
'nodeB': {
'screen_name': interacted_user,
'ff_ratio': i_ff_ratio,
'exists': exists_iuser
},
'weight': interactions['total']
}
self.__network.append(edge)
logging.info('Created a network of {0} nodes and {1} edges'.format(
len(self.__nodes), len(self.__network)))
logging.info('Unknown users {0}'.format(len(self.__unknown_users)))
# save the net in a gefx file for posterior usage
f_name = self.save_network_in_gexf_format(file_name)
logging.info('Saved the network in the file {0}'.format(f_name))
db_net = {'file_name': str(f_name)}
db_net.update(net_query)
self.__dbm_networks.save_record(db_net)
else:
f_net = ret_net[0]
logging.info(
'The network was already generated, please find it at {0}'.
format(f_net['file_name']))
def create_graph(self):
logging.info('Creating the graph, please wait_')
self.__graph = net.DiGraph()
ff_ratio = defaultdict(lambda: 0.0)
# create a directed graph from the edge data and populate a dictionary
# with the friends/followers ratio
for edge in self.__network:
user = edge['nodeA']['screen_name']
interacted_with = edge['nodeB']['screen_name']
num_interactions = edge['weight']
u_ff_ratio = edge['nodeA']['ff_ratio']
self.__graph.add_edge(user,
interacted_with,
weight=int(num_interactions))
ff_ratio[user] = float(u_ff_ratio)
# obtain central node
# degrees = net.degree(self.__graph)
# central_node, max_degree = sorted(degrees, key=itemgetter(1))[-1]
# center the graph around the central node
# ego_graph = net.DiGraph(net.ego_graph(self.__graph, central_node))
return
def get_graph_nodes(self):
return len(self.__nodes)
def get_graph_edges(self):
return len(self.__network)
def get_graph(self):
return self.__graph
def get_node_sizes(self):
return self.__node_sizes
def __get_ffratio(self, screen_name):
query = {
'$or': [{
'user.screen_name': screen_name
}, {
'retweeted_status.user.screen_name': screen_name
}, {
'quoted_status.user.screen_name': screen_name
}]
}
tweet_obj = self.__dbm_tweets.find_record(query)
if tweet_obj:
tweet = tweet_obj['tweet_obj']
if 'retweeted_status' in tweet.keys():
return self.__computer_ff_ratio(
tweet['retweeted_status']['user']['friends_count'],
tweet['retweeted_status']['user']['followers_count'])
elif 'quoted_status' in tweet.keys():
return self.__computer_ff_ratio(
tweet['quoted_status']['user']['friends_count'],
tweet['quoted_status']['user']['followers_count'])
else:
return self.__computer_ff_ratio(
tweet['user']['friends_count'],
tweet['user']['followers_count'])
else:
return None
def save_network_in_gexf_format(self, file_name):
today = datetime.strftime(datetime.now(), '%m/%d/%y')
f_name = pathlib.Path(__file__).parents[1].joinpath(
'sna', 'gefx', file_name + '_' + today + '.gexf')
with open(str(f_name), 'w', encoding='utf-8') as f:
f.write('<?xml version="1.0" encoding="UTF-8"?>\n')
f.write(
'<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" '
'xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" '
'xsi:schemaLocation="http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd" '
'version="1.2">\n')
f.write('<meta lastmodifieddate="{0}">\n'.format(today))
f.write('<creator>NetworkAnalysis</creator>\n')
f.write('<description>{0}</description>\n'.format(file_name))
f.write('</meta>\n')
f.write('<graph mode="static" defaultedgetype="directed">\n')
# add data attributes
f.write('<attributes class="node">\n')
f.write('<attribute id="2" title="ff_ratio" type="float"/>\n')
f.write('<attribute id="5" title="exists" type="float"/>\n')
f.write('</attributes>\n')
# add nodes
f.write('<nodes>\n')
node_id = 0
list_nodes = []
for node_tup in self.__nodes:
node = dict(node_tup)
f.write('<node id="{0}" label="{1}">\n'.format(
node_id, node['screen_name']))
f.write('<attvalues>\n')
f.write('<attvalue for="2" value="{0}"/>\n'.format(
node['ff_ratio']))
f.write('</attvalues>\n')
#f.write('<viz:size value="{0}"/>\n'.format(node['ff_ratio']))
f.write('</node>\n')
node_id += 1
list_nodes.append(node['screen_name'])
f.write('</nodes>\n')
# add edges
f.write('<edges>\n')
edge_id = 0
for edge in list(self.__network):
id_vertexA = list_nodes.index(edge['nodeA']['screen_name'])
id_vertexB = list_nodes.index(edge['nodeB']['screen_name'])
weight = edge['weight']
f.write(
'<edge id="{0}" source="{1}" target="{2}" weight="{3}"/>\n'
.format(edge_id, id_vertexA, id_vertexB, weight))
edge_id += 1
f.write('</edges>\n')
f.write('</graph>\n')
f.write('</gexf>\n')
return f_name