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inital_probability.py
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inital_probability.py
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import networkx as nx
import json
import re
def get_tags_from_tweet(tweet):
word_list=re.findall(r"#(\w+)", tweet)
tag_list=[]
for word in word_list:
if len(word)<1:
continue
tag_list.append(word)
return tag_list
def count_tweet(remain_table,total_table,tag_list,remain_signal):
if remain_signal=='unkown':
return
for tag in tag_list:
if tag in remain_table and remain_signal=='remain':
remain_table[tag]+=1
elif remain_signal=='remain':
remain_table[tag]=1
if tag in total_table:
total_table[tag]+=1
else:
total_table[tag]=1
return
def caculate_probability(remain_table,total_table,inital_probabolity):
for key in remain_table.keys():
probability=float(remain_table[key])/total_table[key]
inital_probabolity[key]=probability
return
origin_file_name='svm_classified.graphml'
result_file_name='inital_probabolity.json'
remain_table={}
total_table={}
inital_probabolity={}
with open(origin_file_name,mode='r') as f:
G=nx.read_graphml(f)
for v in G.nodes():
if 'tweet' not in G.node[v]:
continue
tag_list=get_tags_from_tweet(G.node[v]['tweet'])
count_tweet(remain_table,total_table,tag_list,G.node[v]['brexit'])
caculate_probability(remain_table, total_table, inital_probabolity)
with open(result_file_name, mode='w') as f:
json.dump(inital_probabolity,f,sort_keys=True)