文章目录

• 效果展示
• 介绍
• 软件架构
• 使用说明
• • 导入需要的库
  • 执行main.py

这里以三国演义为例

三国演义.txt
alice_mask.png

效果展示

介绍

Python读取小说文本，绘制词云图，主要人物出场次序，社交网络关系图，章回字数

软件架构

环境准备：Python3
主要模块：networkx wordcloud matplotlib pyecharts imageio jieba

使用说明

导入需要的库

先进行 pip install

#导入networkx，matplotlib包
import networkx as nx
import matplotlib.pyplot as plt
import jieba.posseg as pseg #引入词性标注接口
#导入random包
import random
import codecs
# 导入pyecharts
from pyecharts import options as opts
# pyecharts 柱状图 
from pyecharts.charts import Bar
# pyecharts 词云图
from pyecharts.charts import WordCloud
# pyecharts 折线/面积图
from pyecharts.charts import Line
# 词云
import wordcloud
import imageio

执行main.py

有详细注释

# -*- coding: utf-8 -*-
"""
Created on Wed Jun 23 11:41:01 2021

@author: 陈建兵
"""

#导入networkx，matplotlib包
import networkx as nx
import matplotlib.pyplot as plt
import jieba.posseg as pseg #引入词性标注接口
#导入random包
import random
import codecs
# 导入pyecharts
from pyecharts import options as opts
# pyecharts 柱状图 
from pyecharts.charts import Bar
# pyecharts 词云图
from pyecharts.charts import WordCloud
# pyecharts 折线/面积图
from pyecharts.charts import Line
# 词云
import wordcloud
import imageio

# 定义主要人物的个数(用于人物关系图,人物出场次数可视化图)
mainTop = 15

#读取文本
def read_txt(filepath):
    file=open(filepath,'r+',encoding='utf-8')
    txt=file.read()
    file.close()
    return txt

#获取小说文本
txt = read_txt('三国演义.txt')
#停词文档
def stopwordslist(filepath):  
    stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]  
    return stopwords

#stopwords = stopwordslist('中文停用词库.txt')

excludes = {'将军', '却说', '令人', '赶来', '徐州', '不见', '下马', '喊声', '因此', '未知', '大败', '百姓', '大事', '一军', '之后', '接应', '起兵',
                '成都', '原来', '江东', '正是', '忽然', '原来', '大叫', '上马', '天子', '一面', '太守', '不如', '忽报', '后人', '背后', '先主', '此人',
                '城中', '然后', '大军', '何不', '先生', '何故', '夫人', '不如', '先锋', '二人', '不可', '如何', '荆州', '不能', '如此', '主公', '军士',
                '商议', '引兵', '次日', '大喜', '魏兵', '军马', '于是', '东吴', '今日', '左右', '天下', '不敢', '陛下', '人马', '不知', '都督', '汉中',
                '一人', '众将', '后主', '只见', '蜀兵','马军','黄巾','立功','白发','大吉','红旗','士卒','钱粮','于汉','郎舅', '龙凤', '古之', '白虎',
                '古人云', '尔乃', '马飞报', '轩昂', '史官', '侍臣', '列阵','玉玺','车驾','老夫','伏兵','都尉','侍中','西凉','安民','张曰','文武','白旗',
                '祖宗','寻思'}  # 排除的词汇

#使用精确模式对文本进行分词  
#words = jieba.lcut(txt)
counts = {} # 通过键值对的形式存储词语及其出现的次数

#得到 分词和出现次数
def getWordTimes():
    # 分词，返回词性
    poss = pseg.cut(txt)  
    for w in poss:
        if w.flag != 'nr' or len(w.word) < 2 or w.word in excludes:
            continue  # 当分词长度小于2或该词词性不为nr（人名）时认为该词不为人名
        elif w.word == '孔明' or w.word == '孔明曰' or w.word == '卧龙先生':
            real_word = '诸葛亮'
        elif w.word == '云长' or w.word == '关公曰' or w.word == '关公':
            real_word = '关羽'
        elif w.word == '玄德' or w.word == '玄德曰' or w.word == '玄德甚' or w.word == '玄德遂' or w.word == '玄德兵' or w.word == '玄德领' \
                or w.word == '玄德同' or w.word == '刘豫州' or w.word == '刘玄德':
            real_word = '刘备'
        elif w.word == '孟德' or w.word == '丞相' or w.word == '曹贼' or w.word == '阿瞒' or w.word == '曹丞相' or w.word == '曹将军':
            real_word = '曹操'
        elif w.word == '高祖':
            real_word = '刘邦'
        elif w.word == '光武':
            real_word = '刘秀'
        elif w.word == '桓帝':
            real_word = '刘志'
        elif w.word == '灵帝':
            real_word = '刘宏'
        elif w.word == '公瑾':
            real_word = '周瑜'
        elif w.word == '伯符':
            real_word = '孙策'
        elif w.word == '吕奉先' or w.word == '布乃' or w.word == '布大怒' or w.word == '吕布之':
            real_word = '吕布'
        elif w.word == '赵子龙' or w.word == '子龙':
            real_word = '赵云'
        elif w.word == '卓大喜' or w.word == '卓大怒':
            real_word = '董卓'  # 把相同意思的名字归为一个人
        else:
            real_word = w.word
        counts[real_word] = counts.get(real_word, 0) + 1

getWordTimes()
items = list(counts.items())
#进行降序排列 根据词语出现的次数进行从大到小排序
items.sort(key=lambda x: x[1], reverse=True)

#导出数据
#分词生成人物词频（写入文档）
def wordFreq(filepath,topn):
    with codecs.open(filepath, "w", "utf-8") as f:
        for i in range(topn):
            word, count = items[i]
            f.write("{}:{}\n".format(word, count))


#生成词频文件
wordFreq("三国演义词频_人名.txt",300)

#将txt文本里的数据转换为字典形式
fr = open('三国演义词频_人名.txt', 'r',encoding='utf-8' )
dic = {}
keys = [] # 用来存储读取的顺序
for line in fr:
    # 去空白,并用split()方法返回列表
    v = line.strip().split(':')
    #print("v",v)   # v ['诸葛亮', '1373']
    #拼接字典 {'诸葛亮', '1373'}
    dic[v[0]] = v[1]
    keys.append(v[0])
fr.close()
#输出前几个的键值对
print("人物出现次数TOP",mainTop)
print(list(dic.items())[:mainTop]) 


#　绘图
# 人名列表 (用于人物关系图,pyecharts人物出场次数图)
list_name=list(dic.keys()) #人名
list_name_times=list(dic.values())  #提取字典里的数据作为绘图数据

#可视化人物出场次数
def creat_people_view():
    bar = Bar()
    bar.add_xaxis(list_name[0:mainTop])
    bar.add_yaxis("人物出场次数",list_name_times)
    bar.set_global_opts(title_opts=opts.TitleOpts(title="人物出场次数可视化图", subtitle="三国人物TOP"+str(mainTop)),
                        toolbox_opts=opts.ToolboxOpts(is_show=True),
                        xaxis_opts=opts.AxisOpts(axislabel_opts={"rotate":45}))
    bar.set_series_opts(label_opts=opts.LabelOpts(position="top"))
    bar.render_notebook()    # 在 notebook 中展示
    #make_snapshot(snapshot, bar.render(), "bar.png")
    # 生成 html 文件
    bar.render("三国演义人物出场次数可视化图.html")
    


# 生成词云
def creat_wordcloud():
    bg_pic=imageio.imread('alice_mask.png')
    wc=wordcloud.WordCloud(font_path='c:\Windows\Fonts\simhei.ttf',
                           background_color='white',
                           width=1000,height=800,
                           #stopwords=excludes,# 设置停用词
                           max_words=500,
                           mask=bg_pic # mask参数设置词云形状
                           )
    # 从单词和频率创建词云
    wc.generate_from_frequencies(counts)
    # generate(text)  根据文本生成词云
    #wc.generate(txt)
    # 保存图片
    wc.to_file('三国演义词云_人名.png')
    
    #  显示词云图片
    plt.imshow(wc)
    plt.axis('off')
    plt.show()
 
# 使用pyecharts 的方法生成词云
def creat_wordcloud_pyecharts():
    wordsAndTimes = list(dic.items())
    (
        WordCloud()
        .add(series_name="人物次数", data_pair=wordsAndTimes,
             word_size_range=[20, 100],textstyle_opts=opts.TextStyleOpts(font_family="cursive"),)
        .set_global_opts(title_opts=opts.TitleOpts(title="三国演义词云"))
        .render("三国演义词云_人名.html")
    )

# 使用pyecharts 的方法生成章回字数
def chapter_word():
    # 进行章回切片
    list2 = txt.split("------------")
    chapter_list = [i for i in range((len(list2)))]
    word_list = [len(i) for i in list2]
    
    (
        Line(init_opts=opts.InitOpts(width="1400px", height="700px"))
        .add_xaxis(xaxis_data=chapter_list)
        .add_yaxis(
            series_name="章回字数",
            y_axis=word_list,
            markpoint_opts=opts.MarkPointOpts(
                data=[
                    opts.MarkPointItem(type_="max", name="最大值"),
                    opts.MarkPointItem(type_="min", name="最小值"),
                ]
            ),
            markline_opts=opts.MarkLineOpts(
                data=[opts.MarkLineItem(type_="average", name="平均值")]
            ),
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(title="三国演义章回字数", subtitle=""),
            tooltip_opts=opts.TooltipOpts(trigger="axis"),
            toolbox_opts=opts.ToolboxOpts(is_show=True),
            xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
        )
        .render("三国演义章回字数.html")
    )

#颜色生成
colorNum = len(list_name[0:mainTop])
#print('颜色数',colorNum)
def randomcolor():
    colorArr = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F']
    color = ""
    for i in range(6):
        color += colorArr[random.randint(0,14)]
    return "#"+color

def color_list():
    colorList = []
    for i in range(colorNum):
        colorList.append(randomcolor())
    
    return colorList



# 解决中文乱码
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签

    
#生成人物关系图
def creat_relationship():
    # 人物节点颜色
    colors = color_list()
    Names=list_name[0:mainTop]
    relations={}
    #按段落划分，假设在同一段落中出现的人物具有共现关系
    lst_para=(txt).split('\n')#lst_para是每一段
    for text in lst_para:
        for name_0 in Names:
            if name_0 in text:
                for name_1 in Names:
                    if name_1 in text and name_0!=name_1 and (name_1,name_0) not in relations:
                        relations[(name_0,name_1)]=relations.get((name_0,name_1),0)+1
    maxRela=max([v for k,v in relations.items()])
    relations={k:v /  maxRela for k,v in relations.items()}
    #return relations


    plt.figure(figsize=(15,15))
    # 创建无多重边无向图
    G=nx.Graph()
    for k,v in relations.items():
        G.add_edge(k[0],k[1],weight=v)
    #筛选权重大于0.6的边
    elarge=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight']>0.6]
    #筛选权重大于0.3小于0.6的边
    emidle=[(u,v) for (u,v,d) in G.edges(data=True) if (d['weight']>0.3) & (d['weight']<=0.6)]
    #筛选权重小于0.3的边
    esmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight']<=0.3]
    #设置图形布局
    pos=nx.spring_layout(G) # 用Fruchterman-Reingold算法排列节点（样子类似多中心放射状）
    #设置节点样式
    nx.draw_networkx_nodes(G,pos,alpha=0.8, node_size=1300,node_color=colors)
    #设置大于0.6的边的样式
    nx.draw_networkx_edges(G,pos,edgelist=elarge, width=2.5,alpha=0.9,edge_color='g')
    #0.3~0.6
    nx.draw_networkx_edges(G,pos,edgelist=emidle, width=1.5,alpha=0.6,edge_color='y')
    #<0.3
    nx.draw_networkx_edges(G,pos,edgelist=esmall, width=1,alpha=0.4,edge_color='b',style='dashed')
    nx.draw_networkx_labels(G,pos,font_size=14)
    
    plt.title("《三国演义》主要人物社交关系网络图")
    # 关闭坐标轴
    plt.axis('off')
    
    #保存图表
    plt.savefig('《三国演义》主要人物社交关系网络图.png',bbox_inches='tight')
    plt.show()


def main():
    # 人物出场次数可视化图
    creat_people_view()
    # 词云图
    creat_wordcloud()
    creat_wordcloud_pyecharts()
    # 人物关系图
    creat_relationship()
    # 章回字数
    chapter_word()


if __name__ == '__main__':
    main()