Python Selenium ChromeDriver报错：WebDriver.__init__()意外参数executable_path

一、修复executable_path参数错误 #

Selenium 4.x版本起，移除了executable_path这个初始化参数，需通过Service类指定ChromeDriver路径。具体修改如下：

1. 新增导入语句 #

在原有导入代码中添加Service类：

from selenium.webdriver.chrome.service import Service

2. 修改驱动初始化代码 #

替换原有的驱动创建代码：

# 原代码
driver = webdriver.Chrome(executable_path=chromedriver_path, options=chrome_options)

# 修改后代码
service = Service(chromedriver_path)
driver = webdriver.Chrome(service=service, options=chrome_options)

修复后的完整代码（含元素选择器适配Yelp最新结构） #

!pip install selenium beautifulsoup4 pandas
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from bs4 import BeautifulSoup
import pandas as pd
import os
import time
import random

# Set the path to the ChromeDriver executable
chromedriver_path = "C:\\Users\\5mxz2\\Downloads\\chromedriver\\chromedriver"

# Set the URL of the Yelp page you want to scrape
url = "https://www.yelp.com/biz/gelati-celesti-virginia-beach-2"

# Set the options for Chrome
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument("--headless")
# 添加反爬参数
chrome_options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36")
chrome_options.add_argument("--disable-blink-features=AutomationControlled")

# Create the ChromeDriver instance
service = Service(chromedriver_path)
driver = webdriver.Chrome(service=service, options=chrome_options)

# Load the Yelp page
driver.get(url)
# 随机等待页面加载
time.sleep(random.uniform(2, 4))

# Extract the page source and pass it to BeautifulSoup
soup = BeautifulSoup(driver.page_source, "html.parser")

# Find all review elements on the page（适配Yelp最新评论容器class）
reviews = soup.find_all("div", class_="review__09f24__oHr9V")

# Create empty lists to store the extracted data
review_texts = []
ratings = []
dates = []

# Iterate over each review element
for review in reviews:
    # Extract the review text
    try:
        review_text = review.find("p", class_="comment__09f24__gu0rG").get_text().strip()
    except AttributeError:
        review_text = None
    review_texts.append(review_text)

    # Extract the rating
    try:
        rating = review.find("div", class_="i-stars__09f24__M1AR7").get("aria-label")
    except AttributeError:
        rating = None
    ratings.append(rating)

    # Extract the date
    try:
        date = review.find("span", class_="css-chan6m").get_text().strip()
    except AttributeError:
        date = None
    dates.append(date)

# Create a DataFrame from the extracted data
data = {
    "Review Text": review_texts,
    "Rating": ratings,
    "Date": dates
}
df = pd.DataFrame(data)

# Print the DataFrame
print(df)

# Save the DataFrame as a CSV file
csv_path = os.path.join(os.getcwd(), "yelp_reviews.csv")
df.to_csv(csv_path, index=False)

# Close the ChromeDriver instance
driver.quit()

二、Yelp评论爬取与分析实用建议 #

爬取相关建议 #

• 反爬规避：
  • 除代码中已添加的UA和反检测参数，还可添加--no-sandbox、--disable-dev-shm-usage参数提升稳定性；
  • 不要连续爬取大量页面，每爬1-2页添加3-5秒的随机等待；
  • 若需爬取多个商家，可切换IP或使用代理池，避免IP被封禁。
• 分页处理：当前代码仅爬第一页，可通过定位"Next"按钮（通常class含next-link），循环点击直到按钮不可用，实现全量评论爬取；
• 元素选择器维护：Yelp会频繁更新页面class，若出现数据提取为空的情况，需打开浏览器开发者工具重新定位元素。

Pandas分析建议 #

• 数据清洗：将评分文本转换为数字，方便统计：

  df["Rating"] = df["Rating"].str.extract(r'(\d+)').astype(float)
  

• 基础统计：用df["Rating"].describe()查看评分分布，df["Rating"].value_counts(normalize=True)计算各评分占比；
• 文本分析：统计评论字数分布，或用TextBlob做简单情感分析：

  from textblob import TextBlob
  df["Sentiment"] = df["Review Text"].apply(lambda x: TextBlob(str(x)).sentiment.polarity if x else None)
  

• 时间趋势分析：将日期转为datetime格式，按月份分组查看评分变化：

  df["Date"] = pd.to_datetime(df["Date"], format="%m/%d/%Y")
  monthly_rating = df.groupby(df["Date"].dt.to_period("M"))["Rating"].mean()
  monthly_rating.plot(kind="line", title="Monthly Average Rating")
  

内容的提问来源于stack exchange，提问作者Y0hno