import jieba import jieba.analyse import wordcloud from wordcloud import WordCloud text1=open("text1.txt", "r", encoding="utf-8") line1= text1.read() LIST1=jieba.analyse.extract_tags(line1,10) text2=open("text2.txt", "r", encoding="utf-8") line2= text2.read() LIST2=jieba.analyse.extract_tags(line2,10) a=[x for x in LIST1 if x in LIST2] wc = WordCloud(background_color='white', font_path='D:\Program Files (x86)\Douyu\DYTool\data\Font\内海字体.ttf', width=1000, height=800, ) wc.generate(str(a)) wc.to_file("10.png")

Python利用jieba分词与wordcloud生成标签云图

在代码中，jieba.cut(txt1, cut_all=True)用来对文本进行全模式分词，将整个文本切割成一个个单独的词语。cut_all=True表示使用全模式，意味着jieba会尽可能地切割出所有可能的词语，适合生成词云时获取大量词汇...

jieba与wordcloud实现词云图可视化教程

资源摘要信息:"基于jieba & wordcloud的词云生成" 知识点详细说明: 1. Jieba中文分词库：Jieba是一个用于中文文本的分词Python库，它支持三种分词模式：精确模式、全模式和搜索引擎模式。精确模式试图将句子最精确地...

import pickle from os import path import jieba import jieba.analyse import matplotlib.pyplot as plt from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator import sys sys.path.append('C:\\Users\\李肖\\PycharmProjects\\pythonProject\\text.txt') with open('C:\\Users\\李肖\\PycharmProjects\\pythonProject\\text.txt','r',encoding='utf-8')as fin: text=fin.read() background_Image=plt.imread('main.jpg') print('加载图片成功!') '''设置词云样式''' wc = WordCloud(background_color="black", mask=background_Image, font_path='msyh.ttc', max_words=200, stopwords=STOPWORDS, max_font_size=50, random_state=30) wc.generate_from_text(text) print('开始加载文本') plt.imshow(wc) plt.axis('off') plt.show() d=path.dirname(file) wc.to_file(path.join(d,"h11.jpg")) print('生成词云成功！')不能运行

with open('text.txt', 'r', encoding='utf-8') as fin: text = fin.read() background_Image = plt.imread('main.jpg') print('加载图片成功!') '''设置词云样式''' wc = WordCloud(background_color="black", ...

ky = result['关键词'].replace("[","\n").replace(']','\n').replace("'"," ").replace(",", " ") ky = ky.to_string() keywords = result['关键词'].astype(str) keywords ={k: v.encode('utf-8').decode('utf-8') for k, v in keywords.items()} file = open('1.txt',mode='w',encoding='utf-8') file.write(ky) file.close() jieba.load_userdict("1.txt") title = result['标题'].astype(str) title = {t: l.encode('utf-8').decode('utf-8') for t, l in title.items()} titles = " ".join(title.values()) keywordss = " ".join(keywords.values()) dictionary = jieba.cut(ky) print(",".join(dictionary)) text_analysis = jieba.analyse.extract_tags(keywordss,topK = 100, withWeight=True) title_analysis = jieba.analyse.extract_tags(titles,topK = 100, withWeight=True)

file = open('1.txt', mode='w', encoding='utf-8') file.write(ky) file.close() import jieba jieba.load_userdict("1.txt") keywords = result['关键词'].astype(str) keywords ={k: v.encode('utf-8').decode...

import os import jieba.analyse from textrank4zh import TextRank4Keyword import concurrent.futures import scipy # 定义分块读取函数 def read_in_chunks(file_path, chunk_size=1024 * 1024): with open(file_path, 'r', encoding='utf-8') as f: while True: data = f.read(chunk_size) if not data: break yield data # 定义处理函数 def process_chunk(chunk): # 使用jieba分词提取关键词 jieba_keywords = jieba.analyse.extract_tags(chunk, topK=10, withWeight=True) # 使用textrank4zh提取关键词 tr4w = TextRank4Keyword() tr4w.analyze(chunk, lower=True, window=2) textrank_keywords = tr4w.get_keywords(10, word_min_len=2) # 合并两种方法提取的关键词 keywords = jieba_keywords + textrank_keywords return keywords # 读取文本文件，并按块处理 chunks = [] for chunk in read_in_chunks('cnl_201504.txt'): chunks.append(chunk) # 多线程并行处理 results = [] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(process_chunk, chunk) for chunk in chunks] for future in concurrent.futures.as_completed(futures): results.extend(future.result()) # 合并结果，并按权重降序排序 keywords = {} for keyword, weight in results: if keyword in keywords: keywords[keyword] += weight else: keywords[keyword] = weight keywords = sorted(keywords.items(), key=lambda x: x[1], reverse=True) keywords = [(keyword, weight) for keyword, weight in keywords if len(keyword) > 1][:10] # 输出到txt文件中 with open('output.txt', 'w', encoding='utf-8') as f: for keyword, weight in keywords: f.write(keyword + '\t' + str(weight) + '\n')运行上述代码出现下述问题，请修改代码：AttributeError: module 'networkx' has no attribute 'from_numpy_matrix'

from textrank4zh import TextRank4Keyword import concurrent.futures import scipy import networkx as nx 如果你还没有安装这个库，可以通过以下命令安装： pip install networkx

根据错误：AttributeError: module 'networkx' has no attribute 'from_numpy_matrix'，修改下述代码：import os import jieba.analyse from textrank4zh import TextRank4Keyword import concurrent.futures # 定义分块读取函数 def read_in_chunks(file_path, chunk_size=1024*1024): with open(file_path, 'r', encoding='utf-8') as f: while True: data = f.read(chunk_size) if not data: break yield data # 定义处理函数 def process_chunk(chunk): # 使用jieba分词提取关键词 jieba_keywords = jieba.analyse.extract_tags(chunk, topK=10, withWeight=True) # 使用textrank4zh提取关键词 tr4w = TextRank4Keyword() tr4w.analyze(chunk, lower=True, window=2) textrank_keywords = tr4w.get_keywords(10, word_min_len=2) # 合并两种方法提取的关键词 keywords = jieba_keywords + textrank_keywords return keywords # 读取文本文件，并按块处理 chunks = [] for chunk in read_in_chunks('input.txt'): chunks.append(chunk) # 多线程并行处理 results = [] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(process_chunk, chunk) for chunk in chunks] for future in concurrent.futures.as_completed(futures): results.extend(future.result()) # 合并结果，并按权重降序排序 keywords = {} for keyword, weight in results: if keyword in keywords: keywords[keyword] += weight else: keywords[keyword] = weight keywords = sorted(keywords.items(), key=lambda x: x[1], reverse=True) keywords = [(keyword, weight) for keyword, weight in keywords if len(keyword) > 1][:10] # 输出到txt文件中 with open('output.txt', 'w', encoding='utf-8') as f: for keyword, weight in keywords: f.write(keyword + '\t' + str(weight) + '\n')

with open(file_path, 'r', encoding='utf-8') as f: while True: data = f.read(chunk_size) if not data: break yield data # 定义处理函数 def process_chunk(chunk): # 使用jieba分词提取关键词 jieba_...

from pyecharts.charts import WordCloud from jieba.analyse import extract_tags with open('f:luser_comment.txt',encoding='utf8') as fn: comment_data = fn.readO tags_pairs=extract_tags(comment_data, topK=50. y"'hWeight=True, allowPOS=['n','v','a') with open('f:luser_comment_tags.txt','w+') as fn: for tag, weight in tags pairs: fn.write(tag) fn.write(':') fn.write(str(weight)) fn.write('\n') wc=WordCloudO wc.add("", [list(i) for i in tags_pairs], word_size_range=[5, 400]) wc.render("f:\lwordcloud.html") 要求：写出程序中“输入、输出语句”、主要功能、核心语句?

with open('f:luser_comment.txt', encoding='utf8') as fn: comment_data = fn.readO tags_pairs = extract_tags(comment_data, topK=50, withWeight=True, allowPOS=['n', 'v', 'a']) with open('f:luser_comment...

实现文本文件“corpus.txt”内容的读取，结合jieba实现关键词提取（top20）,并对提取的关键词进行向量化：要求： 1. 使用jieba库 2. 使用sk-learn库 3. 需要加载停用词表“stopword.txt”

with open('corpus.txt', 'r', encoding='utf-8') as file: text_content = file.read() 3. **关键词提取（使用jieba）**: python import jieba.analyse stop_words = set() # 加载停用词表 with ...

如何在from jieba import analyse 批量增加非停用词词表

with open('nonstop_words.txt', 'r', encoding='utf-8') as f: words = f.readlines() 3. 对于从jieba.analyse导入的textrank模块，可以使用下面的代码批量增加非停用词： from jieba import analyse analyse....

用Python，wordcloud和jieba中文词云图制作

with open("text.txt", "r", encoding="utf-8") as f: text = f.read() # 对文本进行分词 words = jieba.cut(text) # 输出分词结果 print(",".join(words)) 在上面的代码中，我们使用了一个停用词文件来过滤...

我需要一串代码，首先剔除停用词stopwordlist添加词表add_word_list，用jieba对一个policy.txt文本进行分词，题词所有除中文外的标点符号，最后生成一个名为data_set的新的文档

with open('policy.txt', 'r', encoding='utf-8') as f: text = f.read() # 分词并去除标点符号 tokens = [char for char in text if char not in string.punctuation] tokens = [token for token in tokens if ...

帮我写一段python代码，要求如下：https://movie.douban.com/subject/1889243/comments?status=P 爬取上述网址中的评论，保存到路径 data/星际穿越.txt --->保存采集的1000条评论绘制词云图要求生成图像的dpi为200；词语数设置为8；提取动词；背景设置为透明色；指定color_func；设定词频和字号大小关联性

with open('stopwords.txt', 'r', encoding='utf-8') as f: for line in f: stop_words.add(line.strip()) # 对评论进行分词 words = jieba.cut('\n'.join(comments), cut_all=False) # 提取动词 tags = jieba....

python中的jieba实现对一个文件夹中的所有文档进行分词，根据停用词表去除停用词，基于文档中的非停用词及其TF-IDF值，生成该文档的词向量，具体代码...

with open('stopwords.txt', 'r', encoding='utf-8') as f: for line in f: stopwords.add(line.strip()) # 分词函数 def cut_words(file_path): with open(file_path, 'r', encoding='utf-8') as f: content =...

要求只使用jieba库实现中文词频统计与文件读写：对test.txt进行中文文本的词频统计，统计文件中出现词频最多的且词语长度不小于3个字符的前10个词语，将词语及其出现的词频数按照词频数递减排序后输出到result.txt文档中。代码要求简单易懂

with open(file_path, 'r', encoding='utf-8') as f: text = f.read() words = tokenize_and_filter(text) counter = Counter(words) # 获取词频最高的前10个词 top_10_words = counter.most_common(10) ...

Python第三方库jieba与wordcloud安装教程

本文档主要介绍了如何在Python环境中安装并使用两个重要的第三方库——jieba和wordcloud。首先，我们关注于jieba，它是一个中文分词库，对于处理中文文本处理任务非常有用。以下是安装和使用jieba的步骤： 1. **...

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