1 text = open("C:三体.txt", "r", encoding="UTF-8").read() # 读取文本
1 import jieba 2 3 4 text = open("C:三体.txt", "r", encoding="UTF-8").read() # 读取文本 5 word_txt = jieba.lcut(text) # 进行中文分词
1 jieba.load_userdict(r'C:三体词汇.txt') # 加入小说分析对象的特有词汇 2 jieba.add_word("量子力学") # 丰富词汇 3 jieba.add_word("万有引力") 
1 # -*- coding: utf-8 -*- 2 import struct 3 import os 4 5 # 拼音表偏移， 6 startPy = 0x1540; 7 8 # 汉语词组表偏移 9 startChinese = 0x2628; 10 11 # 全局拼音表 12 GPy_Table = {} 13 14 # 解析结果 15 # 元组(词频,拼音,中文词组)的列表 16 17 18 # 原始字节码转为字符串 19 def byte2str(data): 20 pos = 0 21 str = '' 22 while pos < len(data): 23 c = chr(struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]) 24 if c != chr(0): 25 str += c 26 pos += 2 27 return str 28 29 # 获取拼音表 30 def getPyTable(data): 31 data = data[4:] 32 pos = 0 33 while pos < len(data): 34 index = struct.unpack('H', bytes([data[pos],data[pos + 1]]))[0] 35 pos += 2 36 lenPy = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 37 pos += 2 38 py = byte2str(data[pos:pos + lenPy]) 39 40 GPy_Table[index] = py 41 pos += lenPy 42 43 # 获取一个词组的拼音 44 def getWordPy(data): 45 pos = 0 46 ret = '' 47 while pos < len(data): 48 index = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 49 ret += GPy_Table[index] 50 pos += 2 51 return ret 52 53 # 读取中文表 54 def getChinese(data): 55 GTable = [] 56 pos = 0 57 while pos < len(data): 58 # 同音词数量 59 same = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 60 61 # 拼音索引表长度 62 pos += 2 63 py_table_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 64 65 # 拼音索引表 66 pos += 2 67 py = getWordPy(data[pos: pos + py_table_len]) 68 69 # 中文词组 70 pos += py_table_len 71 for i in range(same): 72 # 中文词组长度 73 c_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 74 # 中文词组 75 pos += 2 76 word = byte2str(data[pos: pos + c_len]) 77 # 扩展数据长度 78 pos += c_len 79 ext_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 80 # 词频 81 pos += 2 82 count = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0] 83 84 # 保存 85 GTable.append((count, py, word)) 86 87 # 到下个词的偏移位置 88 pos += ext_len 89 return GTable 90 91 92 def scel2txt(file_name): 93 print('-' * 60) 94 with open(file_name, 'rb') as f: 95 data = f.read() 96 97 print("词库名：", byte2str(data[0x130:0x338])) # .encode('GB18030') 98 print("词库类型：", byte2str(data[0x338:0x540])) 99 print("描述信息：", byte2str(data[0x540:0xd40])) 100 print("词库示例：", byte2str(data[0xd40:startPy])) 101 102 getPyTable(data[startPy:startChinese]) 103 getChinese(data[startChinese:]) 104 return getChinese(data[startChinese:]) 105 106 if __name__ == '__main__': 107 # scel所在文件夹路径 108 in_path = r"C:\Users\Administrator\Downloads" #修改为你的词库文件存放文件夹 109 # 输出词典所在文件夹路径 110 out_path = r"C:\Users\Administrator\Downloads\text" # 转换之后文件存放文件夹 111 fin = [fname for fname in os.listdir(in_path) if fname[-5:] == ".scel"] 112 for f in fin: 113 try: 114 for word in scel2txt(os.path.join(in_path, f)): 115 file_path=(os.path.join(out_path, str(f).split('.')[0] + '.txt')) 116 # 保存结果 117 with open(file_path,'a+',encoding='utf-8')as file: 118 file.write(word[2] + '\n') 119 os.remove(os.path.join(in_path, f)) 120 except Exception as e: 121 print(e) 122 pass
1 for word in word_list: 2 if len(word) == 1: 3 continue 4 else: 5 word_list = word_lists.append(word) 6 word_dict[word] = word_dict.get(word, 0)+1 # get()函数返回指定键的值，若没有则返回默认值
1 wd = list(word_dict.items()) # 为了排序，使字典列表化 2 wd.sort(key=lambda x: x[1], reverse=True) # 根据字典的值排序
1 stops_word = open("C:stops_chinese.txt", "r", encoding="UTF-8").read() # 读取停用词文本 2 exclude = {'两个', '东西', '很快', '一种', '这是', '看着', '真的', '发出', '回答', 3 '感觉', '仿佛', '\u3000', '\n','中'} # 自定义停用词 4 stop_list = stops_word.split() 5 stops_all = set(stop_list).union(set(stop_list), exclude) # 求停用词的并集 6 word_list = [element for element in word_txt if element not in stops_all] # 去掉停用词
1 for i in range(20): # 输出前20个高频的词 2 print(wd[i]) 3 word_csv = wd # 生成csv文件 4 pd.DataFrame(data=word_csv[0:20]).to_csv('The_three_body.csv', encoding='UTF-8')
1 from wordcloud import WordCloud 2 3 import matplotlib.pyplot as plt 4 import jieba 5 import pandas as pd 6 7 8 jieba.load_userdict(r'C:三体词汇.txt') # 加入小说分析对象的特有词汇 9 jieba.add_word("量子力学") # 丰富词汇 10 jieba.add_word("万有引力") 11 text = open("C:三体.txt", "r", encoding="UTF-8").read() # 读取小说文本 12 word_txt = jieba.lcut(text) # 进行中文分词 13 # jieba.add_word('') # 添加小说特有词汇 14 stops_word = open("C:stops_chinese.txt", "r", encoding="UTF-8").read() # 读取停用词文本 15 exclude = {'两个', '东西', '很快', '一种', '这是', '看着', '真的', '发出', '回答', 16 '感觉', '仿佛', '\u3000', '\n','中'} # 自定义停用词 17 stop_list = stops_word.split() 18 stops_all = set(stop_list).union(set(stop_list), exclude) # 求停用词的并集 19 word_list = [element for element in word_txt if element not in stops_all] # 去掉停用词 20 word_dict = {} 21 word_lists = [] 22 for word in word_list: 23 if len(word) == 1: 24 continue 25 else: 26 word_list = word_lists.append(word) 27 word_dict[word] = word_dict.get(word, 0)+1 # get()函数返回指定键的值，若没有则返回默认值 28 wd = list(word_dict.items()) # 为了排序，使字典列表化 29 wd.sort(key=lambda x: x[1], reverse=True) # 根据字典的值排序 30 for i in range(20): # 输出前20个高频的词 31 print(wd[i]) 32 word_csv = wd # 生成csv文件 33 pd.DataFrame(data=word_csv[0:20]).to_csv('The_three_body.csv', encoding='UTF-8') 34 35 36 mywc = WordCloud(background_color='black', margin=2).generate(str(word_lists)) 37 plt.imshow(mywc) 38 plt.axis("off") 39 plt.show()