Scrape Naver Related Search Results with Python

What will be scraped

Prerequisites

Basic knowledge scraping with CSS selectors

If you haven't scraped with CSS selectors, there's a dedicated blog post of mine about how to use CSS selectors when web-scraping that covers what it is, pros and cons, and why they're matter from a web-scraping perspective.

CSS selectors declare which part of the markup a style applies to thus allowing to extract data from matching tags and attributes.

Separate virtual environment

If you didn't work with a virtual environment before, have a look at the dedicated Python virtual environments tutorial using Virtualenv and Poetry blog post of mine to get familiar.

In short, it's a thing that creates an independent set of installed libraries including different Python versions that can coexist with each other at the same system thus preventing libraries or Python version conflicts.

📌Note: this is not a strict requirement for this blog post.

Install libraries:

pip install requests, parsel

Reduce the chance of being blocked

There's a chance that a request might be blocked. Have a look at how to reduce the chance of being blocked while web-scraping, there are eleven methods to bypass blocks from most websites.

Full Code

import requests, json
from parsel import Selector  # https://parsel.readthedocs.io/

# https://docs.python-requests.org/en/master/user/quickstart/#passing-parameters-in-urls
params = {
    "query": "minecraft",  # search query
    "where": "web"         # web results. works with nexearch as well
}

# https://docs.python-requests.org/en/master/user/quickstart/#custom-headers
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.87 Safari/537.36",
}

html = requests.get("https://search.naver.com/search.naver", params=params, headers=headers, timeout=30)
selector = Selector(html.text)

related_results = []

# https://www.programiz.com/python-programming/methods/built-in/enumerate
for index, related_result in enumerate(selector.css(".related_srch .keyword"), start=1):
    keyword = related_result.css(".tit::text").get().strip()
    link = f'https://search.naver.com/search.naver{related_result.css("a::attr(href)").get()}'

    related_results.append({
        "position": index,    # 1,2,3..
        "title": keyword,
        "link": link
    })


print(json.dumps(related_results, indent=2, ensure_ascii=False))

Output:

[
  {
    "position": 1,
    "title": "마인크래프트",
    "link": "https://search.naver.com/search.naver?where=nexearch&query=%EB%A7%88%EC%9D%B8%ED%81%AC%EB%9E%98%ED%94%84%ED%8A%B8&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 2,
    "title": "minecraft 뜻",
    "link": "https://search.naver.com/search.naver?where=nexearch&query=minecraft+%EB%9C%BB&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 3,
    "title": "craft",
    "link": "https://search.naver.com/search.naver?where=nexearch&query=craft&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 4,
    "title": "mine",
    "link": "https://search.naver.com/search.naver?where=nexearch&query=mine&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 5,
    "title": "mojang",
    "link": "https://search.naver.com/search.naver?where=nexearch&query=mojang&ie=utf8&sm=tab_she&qdt=0"
  }
]

Alternative solution

Alternatively, you can achieve the same by using Naver Related results API from SerpApi. It is a paid API with a free plan.

It's almost the same, except you don't need to create the parser from scratch, maintain it, how to bypass blocks from Naver or other search engines, figure out which proxy/CAPTCHA providers is reliable, how to scale it.

from serpapi import NaverSearch
import os, json

params = {
    # https://docs.python.org/3/library/os.html#os.getenv
    "api_key": os.getenv("API_KEY"),  # your serpapi api key
    "engine": "naver",                # search engine to parse results from
    "query": "minecraft",             # search query
    "where": "web"                    # web results
}

search = NaverSearch(params)          # where data extraction happens
results = search.get_dict()           # JSON -> Python dictionary

related_results = []

# iterate over "related_results" and extract position, title and link
for related_result in results["related_results"]:
    related_results.append({
        "position": related_result["position"],
        "title": related_result["title"],
        "link": related_result["link"]
    })

print(json.dumps(related_results, indent=2, ensure_ascii=False))

Output:

[
  {
    "position": 1,
    "title": "마인크래프트",
    "link": "https://search.naver.com?where=nexearch&query=%EB%A7%88%EC%9D%B8%ED%81%AC%EB%9E%98%ED%94%84%ED%8A%B8&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 2,
    "title": "minecraft 뜻",
    "link": "https://search.naver.com?where=nexearch&query=minecraft+%EB%9C%BB&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 3,
    "title": "craft",
    "link": "https://search.naver.com?where=nexearch&query=craft&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 4,
    "title": "mine",
    "link": "https://search.naver.com?where=nexearch&query=mine&ie=utf8&sm=tab_she&qdt=0"
  },
  {
    "position": 5,
    "title": "mojang",
    "link": "https://search.naver.com?where=nexearch&query=mojang&ie=utf8&sm=tab_she&qdt=0"
  }
]

Outro

If you have anything to share, any questions, suggestions, or something that isn't working correctly, reach out via Twitter at @dimitryzub, or @serp_api.

Yours, Dmitriy, and the rest of SerpApi Team.

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