参考：http://www.jianshu.com/p/3bd06f8816d7
将生成的网络可视化。下面介绍共现网络的基本原理。（共现网络简单的英文介绍）
实体间的共现是是一种基于统计信息的提取，关系密切的人物往往会在文中的多段连续出现，通过文中以出现的实体（人名），计算不同实体共同出现的比率和次数，设定一个阈值，大于该阈值认为实体间存在某种联系。
- 环境 windows Python3.6
- 模块jieba https://github.com/fxsjy/jieba
- jephi软件
人名字典
http://labfile.oss.aliyuncs.com/courses/677/dict.txt
《釜山行》中文剧本
http://labfile.oss.aliyuncs.com/courses/677/busan.txt
代码：
# -*- coding: utf-8 -*-
import
os, sys
import jieba, codecs, math
import jieba.posseg as pseg names = {} # 姓名字典
relationships = {} # 关系字典
lineNames = [] # 每段内人物关系 # count names
jieba.load_userdict("D:\\ResearchContent\\Exercise_Programm\\PythonExercise\\Python\\dict.txt")
# 加载字典
with
codecs.open("D:\\ResearchContent\\Exercise_Programm\\PythonExercise\\Python\\fushan.txt", "r", "utf8") as f
:
for
line in f.readlines()
:
poss = pseg.cut(line)
# 分词并返回该词词性
lineNames.append([])
# 为新读入的一段添加人物名称列表
for
w in poss
:
if
w.flag
!= "nr" or len
(w.word)
< :
continue # 当分词长度小于2或该词词性不为nr时认为该词不为人名
lineNames[
-
].append(w.word)
# 为当前段的环境增加一个人物
if
names.get(w.word)
is None:
names[w.word] =
0
relationships[w.word] = {}
names[w.word]
+= 1
# 该人物出现次数加 1
# explore relationships
for
line in lineNames:
# 对于每一段
for
name1 in line
:
for
name2 in line:
# 每段中的任意两个人
if
name1 == name2:
continue
if
relationships[name1].get(name2) is None:
# 若两人尚未同时出现则新建项
relationships[name1][name2]=
1
else:
relationships[name1][name2] = relationships[name1][name2]
+ 1
# 两人共同出现次数加 1
# output
with
codecs.open("busan_node.txt", "w", "gbk") as f
:
f.write("Id Label Weight\r\n")
for name, times in names.items()
:
f.write(name
+ " " +
name
+ " " + str
(times)
+ "
\r\n")
with codecs.open("busan_edge.txt", "w", "gbk") as f
:
f.write("Source Target Weight\r\n")
for name, edges in relationships.items()
:
for
v, w in edges.items()
:
if
w
> :
f.write(name
+ " " +
v
+ " " + str
(w)
+ "
\r\n")
参考：
共线网络简单英文介绍https://forec.github.io/2016/10/03/co-occurrence-structure-capture/
Python中文分词：结巴分词http://www.cnblogs.com/kaituorensheng/p/3595879.html
import as 解释：https://www.zhihu.com/question/20871904
修改2