naive

import pandas as pd import jieba # 结巴分词器 df_news = pd.read_csv('val.txt',names=['category','theme','URL','content'],encoding='utf-8',sep='\s+') df_news = df_news.dropna() content = df_news.content.values.tolist() content_S = [] for line in content: current_segment = jieba.lcut(line) if len(current_segment) > 1 and current_segment != '\r\n': content_S.append(current_segment) # print(content_S[1000]) df_content = pd.DataFrame({'content_S': content_S}) # 分完词构造数据 # df_content.head() # 停用词库 stopwords = pd.read_csv('stopwords.txt',index_col=False,sep='\t',quoting=3,names=['stopword'],encoding='utf-8) # 过滤掉停用词 def drop_stopwords(contents, stopwords): contents_clean = [] all_words = [] for line in contents: line_clean = [] for word in line: if word in stopwords: continue line_clean.append(word) all_words.append(str(word)) #所有词库 contents_clean.append(line_clean) return contents_clean, all_words contents = df_content.content_S.values.tolist() stopwords = stopwords.stopword.values.tolist() contents_clean, all_words = drop_stopwords(contents, stopwords) df_content = pd.DataFrame({'contents_clean': contents_clean}) # df_content.head() df_all_words = pd.DataFrame({'all_words': all_words}) # df_all_words.head() # 统计词频 import numpy as np words_count=df_all_words.groupby(by=['all_words'])['all_words'].agg({'count':np.size}) words_count = words_count.reset_index().sort_values(by=['count'], ascending = False) #words_count.head(10) from wordcloud import WordCloud import matplotlib.pyplot as plt %matplotlib inline import matplotlib as mpl mpl.rcParams['figure.figsize'] = (10,6) wordcloud=WordCloud(font_path='simhei.ttf',background_color='white',max_font_size=80) word_frequence = {x[0]:x[1] for x in words_count.head(100).values} wordcloud = wordcloud.fit_words(word_frequence) plt.imshow(wordcloud)

TF-IDF:提取关键词

import jieba.analyse index = 2400 # print(df_news['content'][index]) content_S_str = ''.join(content_S[index]) print(' '.join(jieba.analyse.extract_tags(content_S_str, topK=6, withWeight=False)))

耐克 阿迪达斯 欧洲杯 球衣 西班牙 赞助费

LDA:主题模型

格式要求：list of list形式，分词好的的整个语料

from gensim import corpora, models, similarities #语料库,模型,相似性 import gensim #做映射，相当于词袋 dictionary = corpora.Dictionary(contents_clean) corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean] lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=20) print(lda.print_topic(1, topn=5)) Out: 0.009*"男人" + 0.007*"女人" + 0.007*"中" + 0.007*"说" + 0.004*"学生" for topic in lda.print_topics(num_topics=20, num_words=5): print(topic[1])

……

df_train = pd.DataFrame({'contents_clean':contents_clean, 'label':df_news['category']}) df_train.label.unique() # array(['汽车', '财经', '科技', '健康', '体育', '教育', '文化', '军事', '娱乐', '时尚'], dtype=object) label_mapping = {'汽车':1,'财经':2,'科技':3,'健康':4,'体育':5,'教育':6,'文化':7,'军事':8,'娱乐':9,'时尚':0} df_train['label'] = df_train['label'].map(label_mapping) from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(df_train['contents_clean'].values,df_train['label'].values, random_state=0) # print(x_train[0][1]) words = [] for line_index in range(len(x_train)): try: words.append(' '.join(x_train[line_index])) except: print(line_index,word_index) # words[0] # 文本特征提取-特征数值计算(TF) from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(analyzer='word', max_features=4000, lowercase=False) cv.fit(words) from sklearn.naive_bayes import MultinomialNB # 朴素贝叶斯分类器 classifier = MultinomialNB() classifier.fit(cv.transform(words), y_train) test_words = [] for line_index in range(len(x_test)): try: test_words.append(' '.join(x_test[line_index])) except: print(line_index, word_index) # test_words[0] classifier.score(cv.transform(test_words),y_test)

0.8032

# 文本特征提取-(TF-IDF) from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer(analyzer='word', max_features=4000, lowercase=False) vectorizer.fit(words) classifier = MultinomialNB() classifier.fit(vectorizer.transform(words), y_train) classifier.score(vectorizer.transform(test_words),y_test)

0.8128