Python 基于jieba的三国演义词频分析与可视化

前言

本项目主要利用python的jieba库对三国演义进行词频分析，并通过WordCloud、networkx，matplotlib等库进行可视化分析.

功能函数与展示

获取本地txt文件内容

def getText(filepath):
    f = open(filepath, 'r', encoding = 'utf-8')
    text = f.read()
    f.close()
    return text #返回文本内容

将停用词文件的词读入到列表stopwords中

def stopwordslist(filepath):
    stopwords = [line.strip() for line in open(filepath, 'r', encoding = 'utf-8').readlines()]
    return stopwords

分词并统计词频

def wordFreq(filepath, text, topn):
    #使用lcut()方法对文本分词
    words = jieba.lcut(text.strip())
    #出现频率存放在字典count
    count = {}
    stopwords = stopwordslist(r'C:\Users\Hasee\Desktop/my_stop_words.txt')
    #词频排序
    items = list(count.items())
    items.sort(key = lambda x:x[1], reverse = True)

分词结果

filepath = r'C:\Users\Hasee\Desktop/三国演义.txt'
wordFreq(filepath, getText(filepath), 200)

可以看出，曹操是妥妥的主角（狗头）

词云

f1 = open(r'C:\Users\Hasee\Desktop/三国演义_词频统计.txt')
text = f1.read()
wcloud = wordcloud.WordCloud(background_color = 'white', width = 1000, max_words =500, height = 400, margin =2, font_path = r'C:\Windows\Fonts/SIMHEI.TTF').generate(text)

更换词云形状

#制作词云,更换形状
f1 = open(r'C:\Users\Hasee\Desktop/三国演义_词频统计.txt')
text = f1.read()
bg_pic = imread(r'C:\Users\Hasee\Desktop/star.png')
wcloud = wordcloud.WordCloud(mask = bg_pic, background_color = 'white', width = 800, max_words =800, height = 400, margin =2, font_path = r'C:\Windows\Fonts/SIMHEI.TTF').generate(text)

章回统计

#章回处理
f= open(r'C:\Users\Hasee\Desktop/三国演义.txt', 'r', encoding='utf-8')
s = f.read()
#获取章节标题
lst_chapter = []
chapter = re.findall("第[\u4E00-\u9FA5]+回", s)
#遍历chapter ,存储到lst_chapter
for x in chapter:
    if x not in lst_chapter:
        lst_chapter.append(x)

统计每一回中玄德出现的次数

cnt_xuande1 = []
cnt_xuande2 = []

for ii in range(120):
    start = lst_chapterindex[ii][0]
    end = lst_chapterindex[ii][1]
    cnt_xuande1.append(s[start:end].count('玄德'))
    cnt_xuande2.append(s[start:end].count('刘备'))

    
#绘图
plt.figure(figsize = (20,10))
plt.plot(list(range(len(cnt_xuande1))), np.array(cnt_xuande1)+np.array(cnt_xuande2),'r')
plt.xticks(list(range(len(cnt_xuande1))), rotation=-90)

plt.xlabel('章节数')
plt.ylabel('出现次数')
plt.title('刘备出现次数')

看来托孤白帝城之后就少被后人提及了，果然滚滚长江东逝水

统计“曹贼”“大耳贼”“美髯公”“汉贼”

for ii in range(120):
    start = lst_chapterindex[ii][0]
    end = lst_chapterindex[ii][1]
    cnt_cz.append(s[start:end].count('曹贼'))
    cnt_dez.append(s[start:end].count('大耳贼'))    
    cnt_sb.append(s[start:end].count('美髯公'))    
    cnt_hz.append(s[start:end].count('汉贼'))    

曹贼和汉贼还是多的

三国兴衰

for ii in range(120):
    start = lst_chapterindex[ii][0]
    end = lst_chapterindex[ii][1]
    cnt_cz.append(s[start:end].count('蜀'))
    cnt_dez.append(s[start:end].count('魏'))    
    cnt_sb.append(s[start:end].count('东吴'))    
    cnt_hz.append(s[start:end].count('汉'))  

曹老板的基业还是强大

平均段落数与字数

社交网络

在这里统计前30个出现次数最多的人物

relations = {}
lst_para = s.split('\n') #按段落划分，假设出现在统一段落中人物具有共现关系
for text in lst_para:
    for name1 in Names:
#         for name1 in text:
        for name2 in Names:
            if name1 in text and name2 in text and name1 !=name2 and (name2,name1) not in relations:
                relations[(name1, name2)] = relations.get((name1,name2), 0) +1

换一种布局

pos = nx.circular_layout(G)
#设置结点样式
nx.draw_networkx_nodes(G, pos, alpha = 0.8, node_size=800)

nx.draw_networkx_edges(G, pos, edgelist = elarge, width=2.5, alpha = 0.9, edge_color = 'g')

nx.draw_networkx_edges(G, pos, edgelist = emidle, width=1.5, alpha = 0.6, edge_color = 'y')

nx.draw_networkx_edges(G, pos, edgelist = esmall, width=1, alpha = 0.4, edge_color = 'b', style ='dashed')

代码获取

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