import random import re import regex import time import math import pandas as pd import os import bs4 from bs4 import MarkupResemblesLocatorWarning from datetime import datetime, timedelta from selenium import webdriver from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager from selenium.webdriver.chrome.service import Service as ChromeService from tqdm import tqdm from joblib import Parallel, delayed from utils.cache_clear import delete_cache import warnings def get_url(keyword, start_date, end_date): # 오케이독본점 SEO 고용함. 스팸 개쩜 url = (f"https://search.naver.com/search.naver?where=articleg&query={keyword}+-오케이독본점&oquery={keyword}" f"&ie=utf8&st=rel&date_option=8" f"&date_from={start_date.replace('-','.')}" f"&date_to={end_date.replace('-','.')}" f"&srchby=text&dup_remove=1" f"&cafe_url=&without_cafe_url=" f"&sm=tab_opt&nso=so%3Add%2Cp%3Afrom{start_date.replace('-','')}to{end_date.replace('-','')}&nso_open=1&prdtype=0") return url def remove_tags(html): # parse html content soup = bs4.BeautifulSoup(html, "html.parser") for data in soup(['style', 'script']): # Remove tags data.decompose() # return data by retrieving the tag content return ' '.join(soup.stripped_strings) def infinity_scroll_to_the_bottom(driver): # Automatically scroll the page MAXITER = 2000 scroll_pause_time = 0.1 + random.random() * 2 # Pause between each scroll screen_height = driver.execute_script("return window.screen.height;") # Browser window height i = 1 while i < MAXITER: # Scroll down driver.execute_script(f"window.scrollTo(0, {screen_height * i + random.randint(1, 500)});") i += 1 time.sleep(scroll_pause_time) # Check if reaching the end of the page scroll_height = driver.execute_script("return document.body.scrollHeight;") # print(scroll_height) if screen_height * i > scroll_height: break return driver def scrap_agent(url, keyword, start_date, end_date): # Chrome WebDriver 설치 경로 가져오기 chrome_path = ChromeDriverManager().install() # WebDriver 설정 options = webdriver.ChromeOptions() options.add_experimental_option("excludeSwitches", ["enable-automation"]) options.add_experimental_option("useAutomationExtension", False) options.add_argument('headless') options.add_argument('window-size=1920x1080') options.add_argument("disable-gpu") # options.add_argument("user-agent=Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko") # WebDriver 초기화 driver = webdriver.Chrome(service=ChromeService(chrome_path), options=options) driver.get(url) time.sleep(2) infinity_scroll_to_the_bottom(driver) single_page_search_result = driver.find_elements(By.CSS_SELECTOR, ".lst_view") search_result_plain_html = single_page_search_result[0].get_attribute('innerHTML') formatter = bs4.formatter.HTMLFormatter(indent=1) search_result_plain_html_beautified = bs4.BeautifulSoup(search_result_plain_html, 'html.parser').prettify(formatter=formatter) search_result_contents = regex.findall(r'<li class="bx">(.*?)\n<\/li>', search_result_plain_html_beautified, re.DOTALL) ret = { "url": [], "title": [], "cafe_name": [], "post_date": [], "text": [] } for content in search_result_contents: content = content.replace("\n", "") href = "/".join(str(i) for i in regex.findall(r'href="([^"]+)/([^"]+)/([^"]+)/([^"]+)"', content, re.DOTALL)[0]) title = regex.search(r'<div class="title_area">(.*?) </div>',content) text = regex.search(r' <div class="dsc_area">(.*?) <\/div>',content) cafe_name = regex.search(r' <div class="user_info">(.*?) </div>',content) post_date = regex.search(r'<span class="sub">(.*?)<\/span>',content) title = remove_tags(title.group(1)) # title = title[:-11] # to remove date text = remove_tags(text.group(1)) cafe_name = remove_tags(cafe_name.group(1)) cafe_name = cafe_name[:-11] post_date = remove_tags((post_date.group(1))) ret["url"].append(href) ret["title"].append(title) ret["cafe_name"].append(cafe_name) ret["post_date"].append(post_date) ret["text"].append(text) delete_cache(driver) driver.close() # merged_result = merge_dicts(ret) out_df = pd.DataFrame.from_dict(ret) if not os.path.exists(f"cafe/{keyword}"): os.mkdir(f"cafe/{keyword}") out_df.to_csv(f"cafe/{keyword}/{start_date}-{end_date}.csv", index=False) return ret def merge_dicts(dict_list): # Initialize the result dictionary with empty lists result = { "url": [], "title": [], "blog_name": [], "post_date": [], "text": [] } # Iterate through each dictionary and merge the lists for d in dict_list: for key in result.keys(): result[key].extend(d.get(key, [])) return result def generate_date_range(start_date, end_date, interval): """ :param start_date: start date in datetimestr formatted in %Y-%m-%d :param end_date: end date in datetimestr formatted in %Y-%m-%d :param interval: interval of time in DAYS :return: returns list of time interval that will be feeded into naver_blog_scrapper. for example, [[2023-10-01, 2023-10-07],[2023-10-08, 2023-10-14]] for generate_date_range("2023-10-01", "2023-10-14", 7) Also, this function can handle when the time range of start and end date is not perfectly divisible by interval generate_date_range("2023-10-01", "2023-10-14", 10) will produce output [['2023-10-01', '2023-10-10'], ['2023-10-11', '2023-10-14']] """ # Convert the start and end date strings to datetime objects start = datetime.strptime(start_date, "%Y-%m-%d") end = datetime.strptime(end_date, "%Y-%m-%d") # Generate a range of dates from start to end date_ranges = [] current_date = start while current_date < end: current_end = min(current_date + timedelta(days=interval - 1), end) date_ranges.append([current_date.strftime("%Y-%m-%d"), current_end.strftime("%Y-%m-%d")]) current_date = current_end + timedelta(days=1) return date_ranges def naver_cafe_scrapper(keyword, start_date, end_date, interval, browser_thread_count=1): date_ranges = generate_date_range(start_date, end_date, interval) urls = [] for date_range in date_ranges: urls.append(get_url(keyword, date_range[0], date_range[1])) pass def parallel_scraping(keyword): results = Parallel(n_jobs=browser_thread_count)(delayed(scrap_agent)(url, keyword, date_ranges[i][0], date_ranges[i][1]) for i, url in enumerate(urls)) return results ret = parallel_scraping(keyword) if __name__ == "__main__": naver_cafe_scrapper("선산읍", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("고아읍", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("산동읍", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("도개면", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("장천면", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("무을면", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("해평면", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("옥성면", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("구미 송정동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("원평동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("구미 지산동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("도량동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("선주원남동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("신평1동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("신평2동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("형곡1동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("형곡2동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("구미 비산동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("공단동", start_date="2022-01-01", end_date="2023-10-31", interval=10, browser_thread_count=1) naver_cafe_scrapper("광평동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("상모사곡동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("임오동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("인동동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("진미동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) naver_cafe_scrapper("양표동", start_date="2022-01-01", end_date="2023-10-31", interval=30, browser_thread_count=1) # naver_cafe_scrapper("구미", start_date="2022-01-01", end_date="2023-10-31", interval=1, browser_thread_count=1) # naver_cafe_scrapper("구미시장", start_date="2022-01-01", end_date="2023-10-31", interval=7, browser_thread_count=1)