本文小编为大家详细介绍“Python怎么使用tf-idf算法计算文档关键字权重并生成词云”,内容详细,步骤清晰,细节处理妥当,希望这篇“Python怎么使用tf-idf算法计算文档关键字权重并生成词云”文章能帮助大家解决疑惑,下面跟着小编的思路慢慢深入,一起来学习新知识吧。
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;
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-Z0-9u4e00-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(' ') # 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;
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()
3 最后词云的图片