前言
python中文分析作业，将对《射雕英雄传》进行中文分析，统计人物出场次数、生成词云图片文件、根据人物关系做社交关系网络和其他文本分析等。
对应内容
1.中文分词，统计人物出场次数，保存到词频文件中，文件内容为出场次数最多的前 300 人（可大于 300）的姓名和次数
# -*- coding: utf-8 -*-import jiebadef getText(filepath): # 传入待读取文件的文件名f = open(filepath, "r", encoding='utf-8')text = f.read()f.close()return text # 返回读出的文本数据# 定义停用词库def stopwordslist(filepath):stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]return stopwordsdef wordFreq(filepath, text, topn):words = jieba.lcut(text.strip())counts = {}stopwords = stopwordslist('stop_words.txt')for word in words:if len(word) == 1:continueelif word not in stopwords:if word == "黄蓉道" or word == "黄蓉笑" or word == "黄蓉见" or word == "黄蓉心":word = "黄蓉"elif word == "郭靖道" or word == "靖哥哥" or word == "郭靖心" or word == "郭靖见" or word == "郭靖听" or word == "郭靖大":word = "郭靖"elif word == "老顽童":word = "周伯通"elif word == "老毒物":word = "欧阳锋"elif word == "成吉思汗":word = "铁木真"elif word == "黄老邪":word = "黄药师"counts[word] = counts.get(word, 0) + 1items = list(counts.items())items.sort(key=lambda x: x[1], reverse=True) # 排序f = open(filepath[:-4] + '_词频.txt', "w")for i in range(topn):word, count = items[i]f.writelines("{}\t{}\n".format(word, count))f.close()text = getText('射雕英雄传.txt')wordFreq('射雕英雄传.txt', text, 300)print('统计结束')
2.利用分析结果生成词云图片文性，要求：使用黑体字、背景色为白色、宽度 1000 像素、高度 860 像素
import matplotlib.pyplot as pltimport wordcloudf = open("射雕英雄传_词频.txt", 'r')text = f.read()f.close()# 黑体字、白色背景、宽1000、高860wcloud = wordcloud.WordCloud(font_path=r'C:\Windows\Fonts\simhei.ttf',background_color="white", width=1000,max_words=500,height=860, margin=2).generate(text)wcloud.to_file("射雕英雄传cloud—1.png") # 保存图片# 显示词云图片plt.imshow(wcloud)plt.axis('off')plt.show()
输出词云图片：
3.利用分析结果生成另一种字体词云图片文性
# -*- coding: utf-8 -*-import matplotlib.pyplot as pltimport wordcloudf = open("射雕英雄传_词频.txt", 'r')text = f.read()f.close()# 改字体为华文行楷wcloud = wordcloud.WordCloud(font_path=r'C:\Windows\Fonts\STXINGKA.ttf',background_color="white", width=1000,max_words=500,# mask=bg_pic, # mask参数设置词云形状height=860, margin=2).generate(text)wcloud.to_file("射雕英雄传cloud—2.png") # 保存图片# 显示词云图片plt.imshow(wcloud)plt.axis('off')plt.show()
输出：
4.利用形状，生成特定形状词云图片文性
# -*- coding: utf-8 -*-import matplotlib.pyplot as pltimport wordcloudfrom imageio import imreadbg_pic = imread('star.jpg') # 读入形状图片f = open("射雕英雄传_词频.txt", 'r')text = f.read()f.close()# 更改词云形状wcloud = wordcloud.WordCloud(font_path=r'C:\Windows\Fonts\simhei.ttf',background_color="white", width=1000,max_words=500,mask=bg_pic, # mask参数设置词云形状height=860, margin=2).generate(text)wcloud.to_file("射雕英雄传cloud—3.png") # 保存图片# 显示词云图片plt.imshow(wcloud)plt.axis('off')plt.show()
输出：
5.根据文中人物关系，做社交关系网络并截图保存
# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as pltimport matplotlibf = open('射雕英雄传.txt', 'r', encoding='utf-8')s = f.read()# 生成人物关系权重Names = ['郭靖', '黄蓉', '洪七公', '欧阳锋', '黄药师', '周伯通', '丘处机', '欧阳克', '梅超风', '柯镇恶', '裘千仞', '铁木真','完颜洪烈', '穆念慈', '杨康', '完颜康', '彭连虎', '陆冠英', '拖雷', '杨铁心']matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签relations = {}lst_para = s.split('\n') # 按段落划分，假设在同一段落中出现的人物具有共现关系for text in lst_para:for name1 in Names:if name1 in text:for name2 in Names:if name2 in text and name1 != name2 and (name2, name1) not in relations:relations[(name1, name2)] = relations.get((name1, name2), 0) + 1print(relations.items())maxRela = max([v for k, v in relations.items()])relations = {k: v / maxRela for k, v in relations.items()}print(relations.items(), maxRela)plt.figure(figsize=(15, 15))G = nx.Graph()for k, v in relations.items():G.add_edge(k[0], k[1], weight=v)elarge = [(u, v) for (u, v, d) in G.edges(data=True)if d['weight'] > 0.6]emidle = [(u, v) for (u, v, d) in G.edges(data=True)if (d['weight'] > 0.3) & (d['weight'] <= 0.6)]esmall = [(u, v) for (u, v, d) in G.edges(data=True)if d['weight'] <= 0.3]pos = nx.spring_layout(G)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')nx.draw_networkx_labels(G, pos, font_size=12)plt.axis('off')plt.title("《射雕英雄传》主要人物社交关系网络图")plt.show()
输出：
6.进一步中文分析，自选一种即可，结果截图保存
平均段落和字数
# -*- coding: utf-8 -*-import matplotlib.pyplot as pltimport matplotlibimport re# 分割章回f = open('射雕英雄传.txt', 'r', encoding='utf-8')s = f.read()lst_chapter = []chapter = re.findall("第[\u4E00-\u9FA5]+回", s) # "第([\u4E00-\u9FA5]+)回"返回第和回中间的内容for x in chapter:if x not in lst_chapter and len(x) <= 5:lst_chapter.append(x)print(lst_chapter)lst_start_chapterindex = []for x in lst_chapter:lst_start_chapterindex.append(s.index(x))print(lst_start_chapterindex)lst_end_chapterindex = lst_start_chapterindex[1:] + [len(s)]lst_chapterindex = list(zip(lst_start_chapterindex, lst_end_chapterindex))print(lst_chapterindex)# 计算每一回含有多少段、多少字cnt_chap = []cnt_word = []for ii in range(40):start = lst_chapterindex[ii][0]end = lst_chapterindex[ii][1]cnt_chap.append(s[start:end].count("\n"))cnt_word.append(len(s[start:end]))print(cnt_chap)print(cnt_word)# 字长和段落数的散点图plt.figure(figsize=(8, 6))plt.scatter(cnt_chap, cnt_word)for ii in range(40):plt.text(cnt_chap[ii] - 2, cnt_word[ii] + 100, lst_chapter[ii], fontproperties='SimHei', size=7)plt.xlabel("章节段数", fontproperties='SimHei')plt.ylabel("章节字数", fontproperties='SimHei')plt.title("《射雕英雄传》40回", fontproperties='SimHei')plt.show()
Python中文分析：《射雕英雄传》统计人物出场次数 生成词云图片文件 根据人物关系做社交关系网络和其他文本分析
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