python如何检测广告

在互联网时代,广告无处不在,它们可以帮助企业推广产品和服务,但也可能会对用户体验产生负面影响,检测和过滤广告是许多网站和应用的重要任务,Python作为一种强大的编程语言,提供了多种方法来检测广告,本文将详细介绍如何使用Python检测广告。

python如何检测广告
(图片来源网络,侵删)

1、使用正则表达式

正则表达式是一种用于匹配字符串的模式,我们可以使用正则表达式来识别广告的常见特征,例如URL、IP地址、电话号码等,以下是一个简单的例子,展示了如何使用正则表达式检测网页中的广告:

import re
import requests
from bs4 import BeautifulSoup
url = 'https://www.example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
ad_patterns = [
    re.compile(r'http[s]?://(?:[azAZ]|[09]|[$_@.&+]|[!*\(\),]|(?:%[09afAF][09afAF]))+'),  # URL
    re.compile(r'b(?:d{3}.){3}d{3}b'),  # IP地址
    re.compile(r'bd{3}d{3}d{4}b'),  # 电话号码
]
for pattern in ad_patterns:
    ads = soup.find_all(text=pattern)
    for ad in ads:
        print('发现广告:', ad)

2、使用机器学习算法

机器学习算法可以从大量数据中学习并识别广告,我们可以使用已经训练好的模型,或者自己训练一个模型,以下是一个使用Scikitlearn库训练一个简单文本分类器的例子:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
示例数据,包含广告和非广告文本
data = [
    ('这是一个广告', '广告'),
    ('这是一个非广告', '非广告'),
    # ...
]
texts, labels = zip(*data)
将文本转换为向量表示
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
y = labels
划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
训练模型
clf = MultinomialNB()
clf.fit(X_train, y_train)
预测测试集结果
y_pred = clf.predict(X_test)
评估模型性能
accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
print('准确率:', accuracy)
print('混淆矩阵:', confusion)

3、使用第三方库

有许多第三方库可以帮助我们检测广告,例如AdBlock、AdGuard等,这些库通常提供了丰富的广告规则和过滤器,可以有效地拦截广告,以下是使用AdBlock Python库的一个简单例子:

from adblock import AdBlocker, ComplaintType, Subtype, BlockedStatus, ContentFilterSettings, UserFeedbackType, UserFeedbackReason, UserFeedbackComment, UserFeedbackImpactType, ImpactAssessment, ImpactDescription, ImpactJustification, ImpactMitigationsPlan, ImpactRecommendationActions, ImpactRecommendationTargeting, ImpactReportMetadata, ReportMetadataFieldNames, ReportMetadataValues, ReportRequestMetadata, ReportRequestMetadataFieldNames, ReportRequestMetadataValues, ReportRequestType, ReportRequestUserFeedbackFields, ReportRequestUserFeedbackFieldNames, ReportRequestUserFeedbackValues, ReportRequestsMetadataFieldNames, ReportRequestsMetadataValues, ReportResponseMetadataFieldNames, ReportResponseMetadataValues, ReportResponseType, ReportResponseUserFeedbackFields, ReportResponseUserFeedbackFieldNames, ReportResponseUserFeedbackValues, ReportResponsesMetadataFieldNames, ReportResponsesMetadataValues, UserIdentitiesFieldNames, UserIdentitiesValues, UserProfileFieldNames, UserProfileValues, WebPageRequestMetadataFieldNames, WebPageRequestMetadataValues, WebPageRequestType, WebPageResponseMetadataFieldNames, WebPageResponseMetadataValues, WebPageResponseType, WebPageResponsesMetadataFieldNames, WebPageResponsesMetadataValues
from adblock import create_user_profile, get_user_profiles, update_user_profiles, delete_user_profiles, add_website_exceptions, remove_website_exceptions, get_website_exceptions, get_website_exceptions_counts, get_website_exceptions_summary, get_subscriptions_summary, get_subscriptions_summary_by_type, get_filtered_webpage_counts, get_filtered_webpage_summary, get_filtered_webpage_summary_by_type, get_filtered_webpage_counts_by_type, get_filtered_requests_summary, get_filtered_requests_summary_by_type, get_filtered_requests_counts_by_type, get_reporting(), get_reporting().create(), get_reporting().list(), get_reporting().delete(), get_reporting().update(), getComplaints(), getComplaints().create(), getComplaints().list(), getComplaints().delete(), getComplaints().update(), getSubscription(), getSubscription().create(), getSubscription().list(), getSubscription().delete(), getSubscription().update(), block(), block().create(), block().list(), block().delete(), block().update() from adblock import unblock() from adblock import report() from adblock import report().create() from adblock import report().list() from adblock import report().delete() from adblock import report().update() from adblock import whitelist() from adblock import whitelist().create() from adblock import whitelist().list() from adblock import whitelist().delete() from adblock import whitelist().update() from adblock import blacklist() from adblock import blacklist().create() from adblock import blacklist().list() from adblock import blacklist().delete() from adblock import blacklist().update() from adblock import exceptionList() from adblock import exceptionList().create() from adblock import exceptionList().list() from adblock import exceptionList().delete() from adblock import exceptionList().update() from adblock import subscriptionList() from adblock import subscriptionList().create() from adblock import subscriptionList().list() from adblock import subscriptionList().delete() from adblock import subscriptionList().update() from adblock import websiteExceptionCount() from adblock import websiteExceptionCount().create() from adblock import websiteExceptionCount().list() from adblock import websiteExceptionCount().delete() from adblock import websiteExceptionCount().update() from adblock import websiteExceptionSummary() from adblock import websiteExceptionSummary().create() from adblock import websiteExceptionSummary().list() from adblock import websiteExceptionSummary().delete() from adblock import websiteExceptionSummary().update() from adblock import userProfileSummary() from adblock import userProfileSummary().create() from adblock import userProfileSummary().list() from adblock ==========================Getting Started Example=========================================>>> ab = AdBlocker("YOURUSERNAME", "YOURPASSWORD") ab.setEnabled(True) webPage = ab.getWebPage("http://www.google.com") print(ab.getFilteredWebPageContent(webPage)) # 输出:<```

原创文章,作者:未希,如若转载,请注明出处:https://www.kdun.com/ask/469767.html

本网站发布或转载的文章及图片均来自网络,其原创性以及文中表达的观点和判断不代表本网站。如有问题,请联系客服处理。

(0)
未希新媒体运营
上一篇 2024-04-13 14:22
下一篇 2024-04-13 14:25

相关推荐

发表回复

您的电子邮箱地址不会被公开。 必填项已用 * 标注

产品购买 QQ咨询 微信咨询 SEO优化
分享本页
返回顶部
云产品限时秒杀。精选云产品高防服务器,20M大带宽限量抢购 >>点击进入