新闻数据与任务简介

import pandas as pdimport jjieba

df_news = pd.read_table(‘./data/val.txt‘,names=[‘category‘,‘theme‘,‘URL‘,‘content‘],encoding=‘utf-8‘)
df_news = df_news.dropna() # 缺失值直接drop掉
df_news.head()

content = df_news.content.values.tolist()

分词
content_S = []
for line in content:
  current_segment = jieba.lcut(line) # 分词
  if len(current_segment)>1 and current_segment != ‘\r\n‘: # 换行符
    content_S.append(current_segment)

df_content = pd.DataFrame({‘content_S‘:content_S})
df_content.head()

数据清洗、停用词stopwords = pd.read_csv("stopwords.txt",index_col=False,sep="\t",quoting=3,names=[‘stopword‘],encoding=‘utf-8‘
stopwords.head()

# 通过自定义函数来实现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))    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()

words_count = df_all_words.groupby(by=[‘all_words‘])[‘all_words‘].agg({"count":numpy.size})
words_count = words_count.reset_index().sort_values(by=["count"],ascending=False) # reset_index:重置索引，sort_value:排序
word_count.head()

WordCloud库from wordcloud import WordCloud
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib

matplotlib.rcParams[‘figure.figsize‘] = (10.0,5.0)

wordcloud = WordCloud(font_path="./data/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)

jieba提取关键字import jieba.analyse

index = 1000
print(df_news[‘content‘][index])
content_S_str = "".join(content_S[index])
print("  ”.join(jieba.analyse.extract_tags(content_S_str,topK=5,withWeight=False))) # topK关键词数量

LDA:主题模型
from gensim import corpora,models,similarities
import gensim

# 做映射，相当于词袋
dictionary = corpora.Dictionary(contents_clean) # contents_clean要求:list of list形式（两个list），分词好的整个语料
corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean]

lda = gensim.models.ldamodel.LdaModel(corpus=corpus,id2word=dictionary,num_topics=20) # num_topics:number of topics

print(lda.print_topic(1,topn=5))

for topic in lda.print_topics(num_topics=20,num_words=5): # 20个主题，5个关键词  print(topic[1])

基于贝叶斯算法的新闻分类
df_train = pd.DataFrame({‘contents_clean‘:contents_clean,‘label‘:fd_news[‘category‘]})
df_train.tail()

df_train.label.unique()

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":10}
df_train[‘label‘] = df_train[‘label‘].map(label_mapping)
df_train.head()

from aklearn.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=1)

words = []
for line_index in range(len(x_train)):
  try:
    # x_train[line_index][word_index] = str(x_train[line_index][word_index])
    words.append(‘ ‘.join(x_train[line_index])) # 转换成能够转换成向量的格式
  except:
    print(line_index,word_index)
words[0]

from sklearn.feature_extraction.text import CountVectorizer

vec = CountVectorizer(analyzer=‘word‘,max_features=4000,lowercase=False)
vec.fit(words)

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(vec.transform(words),y_train)

# 测试集
test_words = []
for line_index in range(len(x_test)):
  try:
    # x_train[line_index][word_index] = str(x_train[line_index][word_index])
    test_words.append(‘ ‘.join(x_test[line_index])) # 转换成能够转换成向量的格式
  except:
    print(line_index,word_index)
test_words[0]

classifier.score(vec.transform(test_words),y_test) # 输出：0.804000000000....

CountVectorizer的标准使用方式，跟案例无关from sklearn.feature_extraction.text import CountVectorizer
tests=["dog cat fish","dog cat cat","fish bird",‘bird‘] # 注意一定要将数据转化成这种格式
cv = CountVectorizer()
cv_fit = cv.fit_transform(texts)

print(cv.get_feature_names()) # 不重复的单词

print(cv_fit.toarray())

print(cv_fit.toarray().sum(axis=0))

from sklearn.feature_extraction.text import CountVectorizer
tests=["dog cat fish","dog cat cat","fish bird",‘bird‘] # 注意一定要将数据转化成这种格式
cv = CountVectorizer(ngram_range=(1,4)) # ngram_range进行组合
cv_fit = cv.fit_transform(texts)

print(cv.get_feature_names()) # 不重复的单词

print(cv_fit.toarray())

print(cv_fit.toarray().sum(axis=0))



相比之前，精确度稍微有些提升，只是稍微

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(analyzer=‘word‘,max_features=4000,lowercase=False)
vectorizer.fit(words)

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(vectorizer.transform(words),y_train)

classifier.score(vectorizer.transform(test_words),y_test) # 输出结果：0.8152000000000......