import sys import re import jieba import codecs import gensim import numpy as np import pandas as pd def segment(doc: str): stop_words = pd.read_csv('data/stopwords.txt', index_col=False, quoting=3, names=['stopword'], sep='\n', encoding='utf-8') stop_words = list(stop_words.stopword) reg_html = re.compile(r'<[^>]+>', re.S) # 去掉html标签数字等 doc = reg_html.sub('', doc) doc = re.sub('[０-９]', '', doc) doc = re.sub('\s', '', doc) word_list = list(jieba.cut(doc)) out_str = '' for word in word_list: if word not in stop_words: out_str += word out_str += ' ' segments = out_str.split(sep=' ') return segments def doc2vec(file_name, model): start_alpha = 0.01 infer_epoch = 1000 doc = segment(codecs.open(file_name, 'r', 'utf-8').read()) vector = model.docvecs[doc_id] return model.infer_vector(doc) # 计算两个向量余弦值 def similarity(a_vect, b_vect): dot_val = 0.0 a_norm = 0.0 b_norm = 0.0 cos = None for a, b in zip(a_vect, b_vect): dot_val += a * b a_norm += a ** 2 b_norm += b ** 2 if a_norm == 0.0 or b_norm == 0.0: cos = -1 else: cos = dot_val / ((a_norm * b_norm) ** 0.5) return cos def test_model(file1, file2): print('导入模型') model_path = 'tmp/zhwk_news.doc2vec' model = gensim.models.Doc2Vec.load(model_path) vect1 = doc2vec(file1, model) # 转成句子向量 vect2 = doc2vec(file2, model) print(sys.getsizeof(vect1)) # 查看变量占用空间大小 print(sys.getsizeof(vect2)) cos = similarity(vect1, vect2) print('相似度：%0.2f%%' % (cos * 100)) if __name__ == '__main__': file1 = 'data/corpus_test/t1.txt' file2 = 'data/corpus_test/t2.txt' test_model(file1, file2) 有什么问题，怎么解决

NLP与图像处理实战：Jieba与Gensim应用指南

Gensim是一款重要的Python库，用于进行可扩展的统计语义分析。它能帮助分析文本的语义结构，检索语义相似的文档，并且支持多种主题模型算法，如TF-IDF、LSA、LDA和word2vec。学习者可以访问其官网（<https://...

Python大数据人工智能：Pandas库与Numpy库实战

1. 导入模块：使用import numpy as np引入Numpy库，通常会使用别名np来简化后续的代码。 2. 创建数组：np.array([1, 2, 3, 4])可以创建一个一维数组。 3. reshape方法：通过reshape((2, 2))可以将一维数组转换...

import sys import re import jieba import codecs import gensim import numpy as np import pandas as pd def segment(doc: str): stop_words = pd.read_csv('data/stopwords.txt', index_col=False, quoting=3, names=['stopword'], sep='\n', encoding='utf-8') stop_words = list(stop_words.stopword) reg_html = re.compile(r'<[^>]+>', re.S) # 去掉html标签数字等 doc = reg_html.sub('', doc) doc = re.sub('[０-９]', '', doc) doc = re.sub('\s', '', doc) word_list = list(jieba.cut(doc)) out_str = '' for word in word_list: if word not in stop_words: out_str += word out_str += ' ' segments = out_str.split(sep=' ') return segments def doc2vec(file_name, model): start_alpha = 0.01 infer_epoch = 1000 doc = segment(codecs.open(file_name, 'r', 'utf-8').read()) doc_vec_all = model.infer_vector(doc, alpha=start_alpha, steps=infer_epoch) return doc_vec_all # 计算两个向量余弦值 def similarity(a_vect, b_vect): dot_val = 0.0 a_norm = 0.0 b_norm = 0.0 cos = None for a, b in zip(a_vect, b_vect): dot_val += a * b a_norm += a 2 b_norm += b 2 if a_norm == 0.0 or b_norm == 0.0: cos = -1 else: cos = dot_val / ((a_norm * b_norm) ** 0.5) return cos def test_model(file1, file2): print('导入模型') model_path = 'tmp/zhwk_news.doc2vec' model = gensim.models.Doc2Vec.load(model_path) vect1 = doc2vec(file1, model) # 转成句子向量 vect2 = doc2vec(file2, model) print(sys.getsizeof(vect1)) # 查看变量占用空间大小 print(sys.getsizeof(vect2)) cos = similarity(vect1, vect2) print('相似度：%0.2f%%' % (cos * 100)) if name == 'main': file1 = 'data/corpus_test/t1.txt' file2 = 'data/corpus_test/t2.txt' test_model(file1, file2)

其中，首先使用 jieba 库对文本进行分词，并去除停用词，然后使用 gensim.models.Doc2Vec 中的 infer_vector 方法将文本转化为向量表示，最后使用余弦相似度计算两个向量之间的相似度。该代码中使用了两个测试文件 ...

import pandas as pd import jieba from wordcloud import WordCloud import matplotlib.pyplot as plt from PIL import Image # 读取中间表数据并提取读者ID和图书ID列 df = pd.read_excel('中间表.xlsx') reader_ids = df['读者ID'] book_ids = df['图书ID'] # 根据读者ID和图书ID关联读者信息和图书目录，得到每个读者借阅的图书的书名 readers_info = pd.read_excel('读者信息.xlsx') books_catalog = pd.read_excel('图书目录.xlsx') books_borrowed = books_catalog[books_catalog['图书ID'].isin(book_ids)] borrowed_books_names = books_borrowed['书名'] # 使用jieba进行中文分词 split_words = [] for book_name in borrowed_books_names: words = jieba.lcut(book_name) split_words.extend(words) # 加载停用词表并进行停用词过滤 stop_words_files = ['停用词表1.txt', '停用词表2.txt', '停用词表3.txt'] stop_words = set() for stop_words_file in stop_words_files: with open(stop_words_file, 'r', encoding='utf-8') as f: stop_words |= set(f.read().splitlines()) filtered_words = [word for word in split_words if word not in stop_words] # 加载篮球形状图片并生成词云图 basketball_mask = np.array(Image.open('basketball.png')) wordcloud = WordCloud(font_path='simhei.ttf', background_color='white', mask=basketball_mask).generate(' '.join(filtered_words)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis('off') plt.show() # 获取词频最高的前10个词语 word_counts = pd.Series(filtered_words).value_counts() top_10_words = word_counts.head(10).index.tolist() print("该专业师生最迫切需要学习的知识：", top_10_words)

代码中使用了pandas库来读取和处理Excel文件数据，jieba库进行中文分词，wordcloud库生成词云图，matplotlib库进行图像展示，PIL库进行图片处理。在代码中，使用PIL.Image.open()函数加载了一张名为'basketball....

import jieba from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np import pandas as pd # 读取停用词文件 def read_stopwords(file_path): with open(file_path, 'r', encoding='gbk') as f: stopwords = [line.strip() for line in f] return set(stopwords) # 中文分词 def chinese_word_cut(text, stopwords): words = jieba.cut(text) result = [] for word in words: if word not in stopwords: result.append(word) return " ".join(result) # 读取CSV文件 weibo_data = pd.read_csv('E:\Python自然语言处理\data\weibo_Convid19.csv', sep='\t') df = weibo_data['text_raw'] # 获取停用词集合 stopwords = read_stopwords('E:\Python自然语言处理\data\stopword.txt') # 对每条微博进行分词和去停用词 corpus_list = df.apply(lambda x: chinese_word_cut(x, stopwords)) # 提取关键词 corpus = ' '.join(corpus_list) tfidf = TfidfVectorizer() tf_key = tfidf.fit_transform([corpus]) word = tfidf.get_feature_names() weight = tf_key.toarray()[0] w_sort = np.argsort(-weight) print('Top 20 keywords:') for i in range(20): print(word[w_sort[i]])结果含有表情包，怎么去除

import re def remove_emoji(text): emoji_pattern = re.compile("[" u"\U0001F600-\U0001F64F" # emoticons u"\U0001F300-\U0001F5FF" # symbols & pictographs u"\U0001F680-\U0001F6FF" # transport & map ...

import requests import random from matplotlib import pyplot as plt from selenium import webdriver import time import pandas import csv import jieba import csv import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.cluster import KMeans中库函数的作用

6. pandas：用于数据处理和分析，提供高性能、易用的数据结构和数据分析工具。 7. csv：用于读取和写入CSV文件。 8. jieba：中文分词库，用于将中文文本进行分词处理。 9. sklearn.feature_extraction.text....

from transformers import pipeline, BertTokenizer, BertModel import numpy as np import torch import jieba tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') ner_pipeline = pipeline('ner', model='bert-base-chinese') with open('output/weibo1.txt', 'r', encoding='utf-8') as f: data = f.readlines() def cosine_similarity(v1, v2): return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) def get_word_embedding(word): input_ids = tokenizer.encode(word, add_special_tokens=True) inputs = torch.tensor([input_ids]) outputs = model(inputs)[0][0][1:-1] word_embedding = np.mean(outputs.detach().numpy(), axis=0) return word_embedding def get_privacy_word(seed_word, data): privacy_word_list = [] seed_words = jieba.lcut(seed_word) jieba.load_userdict('data/userdict.txt') for line in data: words = jieba.lcut(line.strip()) ner_results = ner_pipeline(''.join(words)) for seed_word in seed_words: seed_word_embedding = get_word_embedding(seed_word) for ner_result in ner_results: if ner_result['word'] == seed_word and ner_result['entity'] == 'O': continue if ner_result['entity'] != seed_word: continue word = ner_result['word'] if len(word) < 3: continue word_embedding = get_word_embedding(word) similarity = cosine_similarity(seed_word_embedding, word_embedding) print(similarity, word) if similarity >= 0.6: privacy_word_list.append(word) privacy_word_set = set(privacy_word_list) return privacy_word_set 上述代码运行之后，结果为空集合，哪里出问题了，帮我修改一下

import numpy as np import torch import jieba tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') ner_pipeline = pipeline('ner', model...

import pandas as pd import matplotlib import numpy as np import matplotlib.pyplot as plt import jieba as jb import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_selection import chi2 import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB def sigmoid(x): return 1 / (1 + np.exp(-x)) import numpy as np #定义删除除字母,数字，汉字以外的所有符号的函数 def remove_punctuation(line): line = str(line) if line.strip()=='': return '' rule = re.compile(u"[^a-zA-Z0-9\u4E00-\u9FA5]") line = rule.sub('',line) return line def stopwordslist(filepath): stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()] return stopwords df = pd.read_csv('./online_shopping_10_cats/online_shopping_10_cats.csv') df=df[['cat','review']] df = df[pd.notnull(df['review'])] d = {'cat':df['cat'].value_counts().index, 'count': df['cat'].value_counts()} df_cat = pd.DataFrame(data=d).reset_index(drop=True) df['cat_id'] = df['cat'].factorize()[0] cat_id_df = df[['cat', 'cat_id']].drop_duplicates().sort_values('cat_id').reset_index(drop=True) cat_to_id = dict(cat_id_df.values) id_to_cat = dict(cat_id_df[['cat_id', 'cat']].values) #加载停用词 stopwords = stopwordslist("./online_shopping_10_cats/chineseStopWords.txt") #删除除字母,数字，汉字以外的所有符号 df['clean_review'] = df['review'].apply(remove_punctuation) #分词，并过滤停用词 df['cut_review'] = df['clean_review'].apply(lambda x: " ".join([w for w in list(jb.cut(x)) if w not in stopwords])) tfidf = TfidfVectorizer(norm='l2', ngram_range=(1, 2)) features = tfidf.fit_transform(df.cut_review) labels = df.cat_id X_train, X_test, y_train, y_test = train_test_split(df['cut_review'], df['cat_id'], random_state = 0) count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) 已经写好以上代码，请补全train和test函数

def train(X_train_tfidf, y_train): clf = MultinomialNB().fit(X_train_tfidf, y_train) return clf def test(clf, X_test): X_test_counts = count_vect.transform(X_test) X_test_tfidf = tfidf_...

from flask import Flask, request, jsonify import jieba import pandas as pd app = Flask(name) # 读取Excel文件 qa_data = pd.read_excel('questions_answers.xlsx') # 分词函数 def cut_words(text): words = jieba.cut(text) return ' '.join(words) # 匹配问题函数 def match_question(text): # 分词 words = cut_words(text) # 在问题列表中匹配 match = qa_data[qa_data['Question'].apply(lambda x: words in cut_words(x))] if match.empty: return '抱歉，我不知道怎么回答您的问题' else: return match.iloc[0]['Answer'] # 后端接口 @app.route('/api/qa', methods=['POST']) def qa(): # 获取前端传递的问题 question = request.form.get('question') # 进行问题匹配 answer = match_question(question) # 返回结果给前端 return jsonify({'answer': answer}) if name == 'main': app.run(debug=True)里面的EVENT_TYPE_OPENED事件常量是什么

这段代码中的 EVENT_TYPE_OPENED 事件常量并不是在这个代码中定义的，它是在之前提到的 watchdog 库中定义的。在这段代码中并没有使用到 EVENT_TYPE_OPENED 这个常量。可能是你之前提到的 ImportError 错误中...

import pandas as pd data = pd.read_csv(C:\Users\Administrator\Desktop\pythonsjwj\weibo_senti_100k.csv') data = data.dropna(); data.shape data.head() import jieba data['data_cut'] = data['review'].apply(lambda x: list(jieba.cut(x))) data.head() with open('stopword.txt','r',encoding = 'utf-8') as f: stop = f.readlines() import re stop = [re.sub(' |\n|\ufeff','',r) for r in stop] data['data_after'] = [[i for i in s if i not in stop] for s in data['data_cut']] data.head() w = [] for i in data['data_after']: w.extend(i) num_data = pd.DataFrame(pd.Series(w).value_counts()) num_data['id'] = list(range(1,len(num_data)+1)) a = lambda x:list(num_data['id'][x]) data['vec'] = data['data_after'].apply(a) data.head() from wordcloud import WordCloud import matplotlib.pyplot as plt num_words = [''.join(i) for i in data['data_after']] num_words = ''.join(num_words) num_words= re.sub(' ','',num_words) num = pd.Series(jieba.lcut(num_words)).value_counts() wc_pic = WordCloud(background_color='white',font_path=r'C:\Windows\Fonts\simhei.ttf').fit_words(num) plt.figure(figsize=(10,10)) plt.imshow(wc_pic) plt.axis('off') plt.show() from sklearn.model_selection import train_test_split from keras.preprocessing import sequence maxlen = 128 vec_data = list(sequence.pad_sequences(data['vec'],maxlen=maxlen)) x,xt,y,yt = train_test_split(vec_data,data['label'],test_size = 0.2,random_state = 123) import numpy as np x = np.array(list(x)) y = np.array(list(y)) xt = np.array(list(xt)) yt = np.array(list(yt)) x=x[:2000,:] y=y[:2000] xt=xt[:500,:] yt=yt[:500] from sklearn.svm import SVC clf = SVC(C=1, kernel = 'linear') clf.fit(x,y) from sklearn.metrics import classification_report test_pre = clf.predict(xt) report = classification_report(yt,test_pre) print(report) from keras.optimizers import SGD, RMSprop, Adagrad from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM, GRU model = Sequential() model.add(Embedding(len(num_data['id'])+1,256)) model.add(Dense(32, activation='sigmoid', input_dim=100)) model.add(LSTM(128)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary() import matplotlib.pyplot as plt import matplotlib.image as mpimg from keras.utils import plot_model plot_model(model,to_file='Lstm2.png',show_shapes=True) ls = mpimg.imread('Lstm2.png') plt.imshow(ls) plt.axis('off') plt.show() model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=["accuracy"]) model.fit(x,y,validation_data=(x,y),epochs=15)

from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent ...

import pandas as pd import numpy as np import re import jieba from collections import Counter from wordcloud import WordCloud import matplotlib.pyplot as plt# 读取数据 # df = df[['text', 'label']] # df = df.dropna()# 分组 # groups = df.groupby('label')# 加载停用词 stop_words = set() with open('C:/Users/Administrator/Desktop/停用词/stopwords.txt', 'r', encoding='utf-8') as f: for line in f.readlines(): stop_words.add(line.strip())# 定义函数，用于去除停用词和提取关键词 def extract_keywords(text): words = [word for word in jieba.cut(text) if word not in stop_words] return Counter(words).most_common(10)# 提取关键词 keywords = {} for name, group in groups: keywords[name] = [] for text in group['text']: keywords[name].extend(extract_keywords(text)) keywords[name] = dict(keywords[name])# 绘制词云 for name, words in keywords.items(): wordcloud = WordCloud(width=800, height=400, font_path='msyh.ttc').generate_from_frequencies(words) plt.figure(figsize=(12, 8)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.title(name) plt.show() 代码对吗

import numpy as np import re import jieba from collections import Counter from wordcloud import WordCloud import matplotlib.pyplot as plt # 读取数据 df = pd.read_csv('data.csv') df = df[['text', '...

import pandas as pd import jieba stop_words = set() # 定义一个set用于存储停用词 # 读入文件，将每个停用词存储在set中 with open('stopwords.txt', 'r',encoding='utf') as f: for line in f.readlines(): stop_words.add(line.strip()) df1 = pd.read_csv('shuju_new.csv',sep='\t\t',engine='python',encoding='utf-8') cut_comments = [] for text in df1["content"]: seg_list = jieba.cut(text) cut_words = [word for word in seg_list if word not in stop_words and len(word.strip()) > 0] cut_comment = " ".join(cut_words) cut_comments.append(cut_comment) df1['cut_comment'] = pd.Series(cut_comments) df1['cut_comment']

1. 导入 pandas 和 jieba 库。 2. 定义一个空的 set，用于存储停用词。 3. 打开停用词文件，并将每个停用词存储在 set 中。 4. 使用 pandas 的 read_csv() 函数读取一个包含评论的 csv 文件。 5. 对于每个评论，...

给以下这段代码加上结果可视化的功能：from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB import jieba from sklearn.model_selection import train_test_split import numpy as np import matplotlib.pyplot as plt good_comments = [] bad_comments = [] with open('D:\PyCharmProjects\爬虫测试\好评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): good_comments.append(line.strip('\n')) with open('D:\PyCharmProjects\爬虫测试\差评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): bad_comments.append(line.strip('\n')) with open('StopWords.txt', 'r', encoding='utf-8') as f: stopwords = f.read().splitlines() good_words = [] for line in good_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] good_words.append(' '.join(words)) bad_words = [] for line in bad_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] bad_words.append(' '.join(words)) # 将文本转换为向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(good_words + bad_words) y = [1] * len(good_words) + [0] * len(bad_words) # 将数据分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 训练模型 clf = MultinomialNB() clf.fit(X_train, y_train) # 测试模型并计算准确率 pred = clf.predict(X_test) accuracy = sum(pred == y_test) / len(y_test) print('准确率：{:.2%}'.format(accuracy)) # 预测新数据的类别 with open('测试评论.txt', 'r', encoding='utf-8') as f: count = 0 for line in f.readlines(): count += 1 test_text = line.strip('\n') test_words = ' '.join(jieba.cut(test_text, cut_all=False)) test_vec = vectorizer.transform([test_words]) pred = clf.predict(test_vec) if pred[0] == 1: print(count, '好评') else: print(count, '差评')

import numpy as np import matplotlib.pyplot as plt good_comments = [] bad_comments = [] # 读取好评和差评文本 with open('D:\PyCharmProjects\爬虫测试\好评.txt', 'r', encoding='gbk') as f: for line in...

import jieba import numpy import numpy as np from PIL import Image from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt with open("comments.txt","r",encoding="utf-8") as f: comments = f.read() words = jieba.cut(comments) stopwords = set(STOPWORDS) stopwords.add('腾讯') stopwords.add('游戏') filtered_words = [] for word in words: if word not in stopwords: filtered_words.append(word) mask = np.array(Image.open("mask.png")) cloud = WordCloud( font_path="C:\Windows\Fonts\simsun.ttc", background_color="white", mask=mask ).generate(" ".join(filtered_words)) cloud.to_file("词云.png") print("词云绘制成功")这个代码怎么运行错误

import numpy as np from PIL import Image from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt with open("comments.txt","r",encoding="utf-8") as f: comments = f.read() words = ...

以下代码运行结果：import pandas as pd import numpy as np import jieba import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score, confusion_matrix, classification_report from sklearn.model_selection import train_test_split # 读取数据 data = pd.read_csv('medical_records.csv', encoding='gbk') # 数据预处理 def clean_text(text): # 去除数字 text = re.sub(r'\d+', '', text) # 去除英文和标点符号 text = re.sub(r'[a-zA-Z’!"#$%&\'()*+,-./:;<=>?@[\\]^_{|}~]+', '', text) # 去除空格 text = re.sub(r'\s+', '', text) # 分词 words = jieba.cut(text) return ' '.join(words) data['cleaned_text'] = data['text'].apply(lambda x: clean_text(x)) # 特征提取和模型训练 tfidf = TfidfVectorizer() X = tfidf.fit_transform(data['cleaned_text']) y = data['label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) clf = MultinomialNB() clf.fit(X_train, y_train) # 模型评估 y_pred = clf.predict(X_test) print('Accuracy:', accuracy_score(y_test, y_pred)) print('Confusion Matrix:', confusion_matrix(y_test, y_pred)) print('Classification Report:', classification_report(y_test, y_pred))

1. 导入必要的库，包括pandas、numpy、jieba、re、sklearn等。 2. 读取数据，使用pandas的read_csv函数读取csv格式的医疗记录数据。 3. 数据预处理，定义了一个clean_text函数，用于去除数字、英文和标点符号，...

import os import jieba import gensim from langconv import Converter from gensim.corpora import WikiCorpus # 定义LabeledLineSentence类 class LabeledLineSentence(object): def init(self, wkc): self.wkc = wkc self.wkc.metadata = True def iter(self): for content, (page_id, title) in self.wkc.get_texts(): yield gensim.models.doc2vec.TaggedDocument( words=[w for c in content for w in jieba.cut( Converter('zh-hans').convert(c))], tags=[title]) # 代码7-5 def train(): zh_name = 'data/zh-latest-pages-articles.xml.bz2' wkc = WikiCorpus(zh_name, lemmatize=False, dictionary={}) documents = LabeledLineSentence(wkc) model = gensim.models.Doc2Vec(documents, dm=0, dbow_words=1, size=192, window=8, min_count=19, iter=5, workers=8) # 使用Doc2Vec训练段落向量 model.save('tmp/zhwk_news.doc2vec') if name == 'main': if os.path.exists('tmp/zhwk_news.doc2vec') == False: print('开始训练模型') train() print('模型训练完毕') # 代码7-6 model = gensim.models.Doc2Vec.load('tmp/zhwk_news.doc2vec') print(model.dv.similarity('番茄', '西红柿')) # 相似度为0.55 print(model.dv.similarity('货车', '卡车')) # 相似度为0.78

这段代码使用了gensim库中的Doc2Vec模型来训练中文文本，训练时使用了中文维基百科的语料库，使用了jieba分词和langconv进行繁简体转换。训练完成后，保存在本地，并使用gensim中的Doc2Vec.load方法进行加载。最后，...

Python实现文本整句与分句热度统计：基于jieba分词的案例

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本文将介绍如何使用Jieba分词库对《红楼梦》进行词云分析，以此揭示文本中的高频词汇以及潜在的人物关系。在文本挖掘和自然语言处理领域，词云是一种直观展示词语频率的方法。它通过不同大小的字体来表示词频，大...

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