1.需要准备的文件

bird.png：云底图片

sgyy.txt：三国演义原文

tingyong.txt：停用词文件

2.源代码

1.统计词频词性并写入文件中

# 贾高亮# 时间:/3/21 18:36 # 功能# 导入networkx，matplotlib包import reimport networkx as nximport matplotlib.pyplot as pltimport jieba.posseg as pseg # 引入词性标注接口# 导入random包import randomimport codecs# 导入pyechartsfrom pyecharts import options as opts# pyecharts 柱状图from pyecharts.charts import Bar# pyecharts 词云图from pyecharts.charts import WordCloud# 词云import wordcloudimport imageio# 定义主要人物的个数keshihuaTop=10 # 可视化人物图人数mainTop = 100 # 人物词云图人物数peopleTop=10 # 人物关系图# 获取小说文本# 读取文件fn = open('prepare/sgyy.txt', encoding="utf-8")string_data = fn.read() # 读出整个文件fn.close() # 关闭文件# 文本预处理pattern = pile(u'\t|\n|\.|-|:|;|\)|\(|\?|"') # 定义正则表达式匹配模式txt = re.sub(pattern, '', string_data) # 将符合模式的字符去除print('预处理完毕')# 停词文档def stopwordslist(filepath):stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]return stopwordsstopwords = stopwordslist('prepare/tingyong.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# 通过键值对的形式存储词语及其出现的次数counts1 = {} # 存放词性词频counts2={} #存放人物词频# # 生成词频词性文件 def getWordTimes1():cutFinal = pseg.cut(txt)for w in cutFinal:if w.word in stopwords or w.word == None:continueelse:real_word = w.word+'_'+w.flagcounts1[real_word] = counts1.get(real_word, 0) + 1getWordTimes1()items1 = list(counts1.items())# 进行降序排列 根据词语出现的次数进行从大到小排序items1.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 wordFreq1(filepath, topn1):with codecs.open(filepath, "w", "utf-8") as f:for i in range(topn1):word, count = items1[i]f.write("{}:{}\n".format(word, count))# 生成词频文件wordFreq1("output/三国演义词频词性.txt", 300)# 将txt文本里的数据转换为字典形式fr1 = open('output/三国演义词频词性.txt', 'r', encoding='utf-8')dic1 = {}keys1 = [] # 用来存储读取的顺序for line in fr1:# 去空白,并用split()方法返回列表v1 = line.strip().split(':')# print("v",v) # v ['诸葛亮', '1373']# 拼接字典 {'诸葛亮', '1373'}dic1[v1[0]] = v1[1]keys1.append(v1[0])fr1.close()list_name1 = list(dic1.keys()) # 人名list_name_times1 = list(dic1.values()) # 提取字典里的数据作为绘图数据def create_wordproperties():bar1 = Bar()bar1.add_xaxis(list_name1[0:keshihuaTop])bar1.add_yaxis("词语出现次数", list_name_times1)bar1.set_global_opts(title_opts=opts.TitleOpts(title="词频词性可视化图", subtitle="词频词性top10"),xaxis_opts=opts.AxisOpts(axislabel_opts={"rotate": 45}))bar1.set_series_opts(label_opts=opts.LabelOpts(position="top"))# 生成 html 文件bar1.render("output/三国演义词频词性可视化图.html")# 得到 分词和出现次数def getWordTimes2():# 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.wordcounts2[real_word] = counts2.get(real_word, 0) + 1getWordTimes2()items2 = list(counts2.items())# 进行降序排列 根据词语出现的次数进行从大到小排序items2.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 wordFreq2(filepath, topn):with codecs.open(filepath, "w", "utf-8") as f:for i in range(topn):word, count = items2[i]f.write("{}:{}\n".format(word, count))# 生成词频文件wordFreq2("output/三国演义词频_人名.txt", 300)

2.可视化人物出现次数

# 将txt文本里的数据转换为字典形式fr = open('output/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:keshihuaTop])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TOP10"),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("output/三国演义人物出场次数可视化图.html")

3.生成云词

# 生成词云def creat_wordcloud():bg_pic = imageio.imread('prepare/bird.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(counts2)# generate(text) 根据文本生成词云# wc.generate(txt)# 保存图片wc.to_file('output/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("output/三国演义词云_人名.html"))# 颜色生成colorNum = len(list_name[0:peopleTop])# 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 "#" + colordef 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

4.生成人物关系图

# 生成人物关系图def creat_relationship():# 人物节点颜色colors = color_list()Names = list_name[0:peopleTop]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) + 1maxRela = max([v for k, v in relations.items()])relations = {k: v / maxRela for k, v in relations.items()}# return relationsplt.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')# 0.3~0.6nx.draw_networkx_edges(G, pos, edgelist=emidle, width=1.5, alpha=0.6, edge_color='y')# <0.3nx.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('output/《三国演义》主要人物社交关系网络图.png', bbox_inches='tight')plt.show()def main():#生成词频词性文件create_wordproperties()# 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()if __name__ == '__main__':main()

3.运行结果截图

1.统计词频词性并写入文件中

2.可视化人物出现次数

3.生成云词

4.生成人物关系图

感谢观看！

如果觉得《利用python语言分析小说的词频 词性 词云 小说人物出场次数排序以及小说人物关系》对你有帮助，请点赞、收藏，并留下你的观点哦！