谣言识别系统（Python）：爬虫（bs+rq）+数据处理（jieba分词）+分类器（贝叶斯）

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谣言识别系统（Python）：爬虫（bs+rq）+数据处理（jieba分词）+分类器（贝叶斯）

简介

谣言识别系统是新闻分类系统的后续，这次我补充了正确新闻的数据集，为了体现新闻的绝对正确性，我爬取了澎湃新闻的数据。

谣言的数据集爬取与处理请参考我的新闻处理系统的数据集，请看点开下面的网址。

http://blog.csdn.net/sileixinhua/article/details/74943336

所有的数据集和代码，结果截图都上传至github

https://github.com/sileixinhua/News-classification/

谣言数据集为false，有3183个。

非谣言新闻数据集为true，有1674个。

这个实验结果是99%，我想结果是过于高了，产生了过拟合。可能谣言新闻都是生活类的，非谣言新闻因为都是澎湃新闻的原因，所以用两类完全不同用词的新闻，用贝叶斯也很好区分分类。

开发环境

Beautiful Soup 4.4.0 文档: http://beautifulsoup.readthedocs.io/zh_CN/latest/#id28

Requests : http://cn.python-requests.org/zh_CN/latest/

Python3

sklearn ：http://scikit-learn.org/stable/

Windows10

sublime

jieba分词

澎湃新闻的新闻爬去页面分析

图1：澎湃新闻主页页面

图2：澎湃新闻的新闻主题内容页面

图3：澎湃新闻的新闻主题内容页面的新闻标签内容

爬虫策略：

由于新闻内容全部都是在news_txt类名标签中，所以也很好处理，直接

soup_text.find_all(["news_txt"])

获取新闻内容即可。

代码

澎湃新闻的爬取和处理

# 2017年7月13日15:27:02
# silei
# 爬虫目标网站：http://www.thepaper.cn/newsDetail_forward_
# 获取信息BeautifulSoup+request
# 正确新闻的爬去，分词，去停用词# -*- coding:UTF-8 -*-from urllib import request
from bs4 import BeautifulSoup
import re
import sys
import codecs
import jieba
import requestsif __name__ == "__main__":   text_file_number = 0web_url_number = 1701736while web_url_number < 1731414 :get_url = 'http://www.thepaper.cn/newsDetail_forward_'+str(web_url_number)   head = {}   #设置头head['User-Agent'] = 'Mozilla/5.0 (Linux; Android 4.1.1; Nexus 7 Build/JRO03D) AppleWebKit/535.19 (KHTML, like Gecko) Chrome/18.0.1025.166  Safari/535.19'# 模拟浏览器模式，定制请求头download_req_get = request.Request(url = get_url, headers = head)# 设置Requestr = requests.get(get_url)print(get_url)print(r.status_code)download_response_get = request.urlopen(download_req_get)# 设置urlopen获取页面所有内容download_html_get = download_response_get.read().decode('UTF-8','ignore')# UTF-8模式读取获取的页面信息标签和内容soup_text = BeautifulSoup(download_html_get, 'lxml')soup_text.find_all(["news_txt"])# BeautifulSoup读取页面html标签和内容的信息web_text = re.compile("<[^>]+>")content=web_text.sub("", str(soup_text))if soup_text == "" :print('字符串为空')continue# 去除页面标签stoplist = {}.fromkeys([content.strip() for content in open("../data/stopword.txt",encoding= 'UTF-8') ])  # 读取停用词在列表中seg_list = jieba.lcut(content,cut_all=False)# jieba分词精确模式seg_list = [word for word in list(seg_list) if word not in stoplist]  # 去除停用词# print("Default Mode:", "/ ".join(seg_list))file_write = codecs.open('../data/train_data_news/true/'+str(text_file_number)+'.txt','w','UTF-8')# 将信息存储在本地for i in range(len(seg_list)):file_write.write(str(seg_list[i])+'\n')file_write.close()print('写入成功')text_file_number = text_file_number + 1web_url_number = web_url_number + 1

谣言分类识别

# 时间：2017年7月13日17:10:27
# silei
# 正确的新闻个数1674#coding: utf-8
import os
import time
import random
import jieba
import nltk
import sklearn
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pylab as pl
import matplotlib.pyplot as pltdef MakeWordsSet(words_file):words_set = set()with open(words_file, 'r', encoding='UTF-8') as fp:for line in fp.readlines():word = line.strip()if len(word)>0 and word not in words_set: # 去重words_set.add(word)return words_setdef TextProcessing(folder_path, test_size=0.2):folder_list = os.listdir(folder_path)data_list = []class_list = []# 类间循环for folder in folder_list:new_folder_path = os.path.join(folder_path, folder)files = os.listdir(new_folder_path)# 类内循环j = 0for file in files:if j > 410: # 每类text样本数最多100breakwith open(os.path.join(new_folder_path, file), 'r', encoding='UTF-8') as fp:raw = fp.read()# print raw## --------------------------------------------------------------------------------## jieba分词# jieba.enable_parallel(4) # 开启并行分词模式，参数为并行进程数，不支持windowsword_cut = jieba.cut(raw, cut_all=False) # 精确模式，返回的结构是一个可迭代的genertorword_list = list(word_cut) # genertor转化为list，每个词unicode格式# jieba.disable_parallel() # 关闭并行分词模式# print word_list## --------------------------------------------------------------------------------data_list.append(word_list)class_list.append(folder)j += 1## 划分训练集和测试集# train_data_list, test_data_list, train_class_list, test_class_list = sklearn.cross_validation.train_test_split(data_list, class_list, test_size=test_size)data_class_list = list(zip(data_list, class_list))random.shuffle(data_class_list)index = int(len(data_class_list)*test_size)+1train_list = data_class_list[index:]test_list = data_class_list[:index]train_data_list, train_class_list = zip(*train_list)test_data_list, test_class_list = zip(*test_list)# 统计词频放入all_words_dictall_words_dict = {}for word_list in train_data_list:for word in word_list:if word in all_words_dict:  all_words_dict[word] += 1else:all_words_dict[word] = 1# key函数利用词频进行降序排序all_words_tuple_list = sorted(all_words_dict.items(), key=lambda f:f[1], reverse=True) # 内建函数sorted参数需为listall_words_list = list(zip(*all_words_tuple_list))[0]return all_words_list, train_data_list, test_data_list, train_class_list, test_class_listdef words_dict(all_words_list, deleteN, stopwords_set=set()):# 选取特征词feature_words = []n = 1for t in range(deleteN, len(all_words_list), 1):if n > 1000: # feature_words的维度1000break# print all_words_list[t]if not all_words_list[t].isdigit() and all_words_list[t] not in stopwords_set and 1<len(all_words_list[t])<5:feature_words.append(all_words_list[t])n += 1return feature_wordsdef TextFeatures(train_data_list, test_data_list, feature_words, flag='nltk'):def text_features(text, feature_words):text_words = set(text)## -----------------------------------------------------------------------------------if flag == 'nltk':## nltk特征 dictfeatures = {word:1 if word in text_words else 0 for word in feature_words}elif flag == 'sklearn':## sklearn特征 listfeatures = [1 if word in text_words else 0 for word in feature_words]else:features = []## -----------------------------------------------------------------------------------return featurestrain_feature_list = [text_features(text, feature_words) for text in train_data_list]test_feature_list = [text_features(text, feature_words) for text in test_data_list]return train_feature_list, test_feature_listdef TextClassifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag='nltk'):## -----------------------------------------------------------------------------------if flag == 'nltk':## nltk分类器train_flist = zip(train_feature_list, train_class_list)test_flist = zip(test_feature_list, test_class_list)classifier = nltk.classify.NaiveBayesClassifier.train(train_flist)# print classifier.classify_many(test_feature_list)# for test_feature in test_feature_list:#     print classifier.classify(test_feature),# print ''test_accuracy = nltk.classify.accuracy(classifier, test_flist)elif flag == 'sklearn':## sklearn分类器classifier = MultinomialNB().fit(train_feature_list, train_class_list)# print classifier.predict(test_feature_list)# for test_feature in test_feature_list:#     print classifier.predict(test_feature)[0],# print ''test_accuracy = classifier.score(test_feature_list, test_class_list)else:test_accuracy = []return test_accuracyif __name__ == '__main__':print("start")## 文本预处理folder_path = 'C:\\Code\\uwasa\\data\\train_data_news'all_words_list, train_data_list, test_data_list, train_class_list, test_class_list = TextProcessing(folder_path, test_size=0.2)# 生成stopwords_setstopwords_file = 'C:\\Code\\uwasa\\data\\stopword.txt'stopwords_set = MakeWordsSet(stopwords_file)## 文本特征提取和分类# flag = 'nltk'flag = 'sklearn'deleteNs = range(0, 1000, 20)test_accuracy_list = []for deleteN in deleteNs:# feature_words = words_dict(all_words_list, deleteN)feature_words = words_dict(all_words_list, deleteN, stopwords_set)train_feature_list, test_feature_list = TextFeatures(train_data_list, test_data_list, feature_words, flag)test_accuracy = TextClassifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag)test_accuracy_list.append(test_accuracy)print(test_accuracy_list)# 结果评价plt.figure()plt.plot(deleteNs, test_accuracy_list)plt.title('Relationship of deleteNs and test_accuracy')plt.xlabel('deleteNs')plt.ylabel('test_accuracy')plt.savefig('result_rumor.png')print("finished")

结果

感想

由于数据集的原因产生了过拟合，有兴趣的同学可以再收集一些新闻，我的两个数据集一个生活养生类的谣言，一个是澎湃新闻，两者差距太大，所以分类结果会过高。

不知不觉从四月开学到现在三个多月过去了，每周的开会和研究报告，学习了整本的《python machine learning》，但是代码还没有全部实现完，马上回家要把PDF书看完，然后回来之后再接着找点实际的数据处理处理。

现在我关注了很多最新论文解说的公众号，的确能有效提高效率，但是我还是找点论文看，英语不能落下。

下一阶段计划有空把Python的网络编程和go语言学习一下。

加油。

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