def connect_users_cities(graph, args):
city_dict = graph_util.query_label_to_dict(graph, "City", "name")
users = graph.nodes.match("User")
relationships = []
print("Creating IS_FROM relationships..")
for user in tqdm(users):
for city in user['city']:
relationships.append(Relationship(user, "IS_FROM",
city_dict[city]))
print("Done.")
print("Writing %d relationships.. " % len(relationships))
graph_util.create_in_batch(graph, relationships)
print("Done.")
def write_hydrated_tweets(graph, args):
print("Reading tweets..")
tweets = None
with open("../data/tweets_fully_hydrated.json", encoding='utf-8',
mode='r') as f:
tweets = json.load(f)
print("Done.")
print("Creating Tweet nodes..")
tweet_nodes = []
with tqdm(total=len(tweets)) as pbar:
for tweet in tweets:
tweet_nodes.append(create_hydrated_tweet(tweet))
pbar.update(1)
print("Done.")
print("Writing %d tweets to database.." % len(tweet_nodes))
graph_util.create_in_batch(graph, tweet_nodes)
print("Done.")
def write_users(graph, args):
print("Reading Userobjects..")
users = None
with open("../data/users_with-loc_with-gender.json",
encoding='utf-8',
mode='r') as f:
inf = f.read()
users = json.loads(inf)
print("Read.")
user_nodes = []
print("Creating User nodes..")
for user in tqdm(users):
user_nodes.append(create_user_node(user))
print("Done.")
print("Writing %d users to database.." % len(user_nodes))
graph_util.create_in_batch(graph, user_nodes)
print("Done.")
def write_tweets(graph, args):
print("WARNING, DEPRECTED")
print("Reading tweets..")
tweets = None
with open("../../Datar/twitter/180524_data_posstagged.pickle",
mode='rb') as f:
data = f.read()
tweets = pickle.loads(data)
tweets.fillna("N/A", inplace=True) # stop neo4j from breaking!!
print("Done.")
print("Creating Tweet nodes..")
tweet_nodes = []
with tqdm(total=len(tweets)) as pbar:
for index, tweet in tweets.iterrows():
tweet_nodes.append(create_tweet_node(tweet))
pbar.update(1)
print("Done.")
print("Writing %d tweets to database.." % len(tweet_nodes))
graph_util.create_in_batch(graph, tweet_nodes)
print("Done.")
def annotate_politicians(graph, args):
with open("../data/politician_user_handles.json",
encoding='utf-8',
mode='r') as f:
inf = f.read()
temp = json.loads(inf)
parties = {}
politicians = {}
## flip the dict around to have things more readable
for party in temp.keys():
parties[party] = Node('Party',
name=party) ## create party nodes along the way
for politician in temp[party]:
politicians[politician] = party
relationships = []
graph_util.create_in_batch(graph, list(parties.values()))
print("Done.")
print("Creating party membership relations..")
for politician in tqdm(politicians.keys()):
politician_node = graph.nodes.match("User").where(
"_.screen_name = \"%s\"" % politician).first()
if politician_node != None:
### this needs to be done when updating anything, this is bullshit.
graph.merge(politician_node)
politician_node.add_label("Politician")
graph.push(politician_node)
relationships.append(
Relationship(politician_node, "IS_MEMBER_OF",
parties[politicians[politician]]))
print("Done.")
print("Writing %d party membership relations to database..")
graph_util.create_in_batch(graph, relationships)
print("Done.")
def connect_tweets_users(graph, args):
relationships = []
user_nodes = graph_util.query_label_to_dict(graph, "User", "screen_name")
tweet_nodes = graph.nodes.match("Tweet")
print("Creating relationships between users via tweets..")
for tweet in tqdm(tweet_nodes):
if tweet['screen_name'] in user_nodes.keys():
user = user_nodes[tweet['screen_name']] # relate the tweet
relationships.append(Relationship(user, "TWEETED", tweet))
if isinstance(tweet['mentions'], list):
for mention in tweet['mentions']:
if mention in user_nodes.keys():
mentioned_user = user_nodes[
mention] # grabbing the user object by screen_name
relationships.append(
Relationship(tweet, "MENTIONS", mentioned_user))
print("Done.")
#### lets write mentions
print("Writing %d relationships to database.." % len(relationships))
graph_util.create_in_batch(graph, relationships)
print("Done.")
def write_locations(graph, args):
#### and let's write the location community.
def create_state_node_dict(states):
result = {}
for state in set(states):
result[state] = Node("State", name=state)
return result
def load_city_mapping():
""" closed in function to get data
"""
city_mapping = {}
find_state = re.compile("(\(\w{2}\))")
find_city = re.compile("(.+ )")
with open("../data/german_cities_raw.txt", encoding='utf-8',
mode='r') as f:
for entry in f:
city = find_city.findall(entry)[0].strip()
state = find_state.findall(entry)[0].replace("(", "").replace(
")", "")
city_mapping[city] = state
return city_mapping
city_mapping = load_city_mapping()
cities = []
states = []
for user in graph.nodes.match("User"):
cities.extend(user['city'])
states.extend(user['state'])
states = create_state_node_dict(states)
print("Writing state nodes..")
graph_util.create_in_batch(graph, list(states.values()))
print("Done.")
city_nodes = []
for city in set(cities):
city_nodes.append(Node("City", name=city))
print("Creating city nodes..")
graph_util.create_in_batch(graph, city_nodes)
print("Done.")
city_nodes
city_to_states = []
for city_node in city_nodes:
city_to_states.append(
Relationship(city_node, "IS_IN",
states[city_mapping[city_node['name']]]))
print("Creating city to state relationships..")
graph_util.create_in_batch(graph, city_to_states)
print("Done.")
