-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathwordcloud.py
More file actions
51 lines (44 loc) · 1.64 KB
/
wordcloud.py
File metadata and controls
51 lines (44 loc) · 1.64 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
import jieba
from pyecharts.charts import WordCloud
import jieba.posseg as pseg
# 读取文本文件
def read_text_file(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
text = file.read()
return text
# 使用jieba进行分词并识别命名实体
def tokenize_and_recognize_entities(text):
words_with_entities = []
words = pseg.cut(text)
for word, flag in words:
# 只保留词性为nr（人名）、ns（地名）、nt（机构名）的词语
if flag in ['nr', 'ns', 'nt']:
words_with_entities.append(word)
return words_with_entities
# 统计词频
def count_word_frequency(word_list, min_length=2):
word_freq = {}
for word in word_list:
if len(word) < min_length:
continue
if word in word_freq:
word_freq[word] += 1
else:
word_freq[word] = 1
return word_freq
# 生成词云
def generate_word_cloud(word_freq, output_file):
# transform word_freq to list of tuples
word_freq = list(word_freq.items())
# sort the list by word frequency
word_freq.sort(key=lambda x: x[1], reverse=True)
cloud = WordCloud()
cloud.add('', word_freq[:50])
cloud.render(output_file)
if __name__ == "__main__":
file_path = 'data/西游记.txt' # 替换为你的文件路径
output_file = 'homework1/wordcloud.html' # 生成的词云图文件路径
text = read_text_file(file_path)
words_with_entities = tokenize_and_recognize_entities(text)
word_freq = count_word_frequency(words_with_entities)
generate_word_cloud(word_freq, output_file)