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Python 使用tf-idf算法计算文档关键字权重并生成词云的方法

时间：2023-03-17来源：系统城装机大师作者：佚名

1. 根据tf-idf计算一个文档的关键词或者短语：

代码如下：

注意需要安装pip install sklean；

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58 from re import split
from jieba.posseg import dt
from sklearn.feature_extraction.text import TfidfVectorizer
from collections import Counter
from time import time
import jieba

#pip install sklean

FLAGS = set('a an b f i j l n nr nrfg nrt ns nt nz s t v vi vn z eng'.split())

def cut(text):
for sentence in split('[^a-zA-Z0-9\u4e00-\u9fa5]+', text.strip()):
for w in dt.cut(sentence):
if len(w.word) > 2 and w.flag in FLAGS:
yield w.word

class TFIDF:
def __init__(self, idf):
self.idf = idf

@classmethod
def train(cls, texts):
model = TfidfVectorizer(tokenizer=cut)
model.fit(texts)
idf = {w: model.idf_[i] for w, i in model.vocabulary_.items()}
return cls(idf)

def get_idf(self, word):
return self.idf.get(word, max(self.idf.values()))

def extract(self, text, top_n=10):
counter = Counter()
for w in cut(text):
counter[w] += self.get_idf(w)
#return [i[0:2] for i in counter.most_common(top_n)]
return [i[0] for i in counter.most_common(top_n)]

if __name__ == '__main__':
t0 = time()
with open('./nlp-homework.txt', encoding='utf-8')as f:
_texts = f.read().strip().split('\n')
# print(_texts)
tfidf = TFIDF.train(_texts)
# print(_texts)
for _text in _texts:
seq_list=jieba.cut(_text,cut_all=True) #全模式
# seq_list=jieba.cut(_text,cut_all=False) #精确模式
# seq_list=jieba.cut_for_search(_text,) #搜索引擎模式
# print(list(seq_list))
print(tfidf.extract(_text))
with open('./resultciyun.txt','a+', encoding='utf-8') as g:
for i in tfidf.extract(_text):
g.write(str(i) + " ")
print(time() - t0)

2. 生成词云：

代码如下：

注意需要安装pip install wordcloud；
以及为了保证中文字体正常显示，需要下载SimSun.ttf字体，并且将这个字体包也放在和程序相同的目录下；

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11 from wordcloud import WordCloud
filename = "resultciyun.txt"
with open(filename) as f:
resultciyun = f.read()

wordcloud = WordCloud(font_path="simsun.ttf").generate(resultciyun)
# %pylab inline
import matplotlib.pyplot as plt
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()