The keywords "low price" and "zero" have emerged as popular restaurant keywords
An analysis of tens of thousands of restaurant-related posts on Naver blogs revealed an interesting shift.
Changes in Restaurant Keywords in 2024 vs 2025
| 순위 | 2024년 상반기 | 2025년 상반기 | 변화 |
|------|--------------|--------------|------|
| 1 | 마라 | 저당/제로 | |
| 2 | 오마카세 | 마라 | ↓ |
| 3 | 로제 | 로컬 맛집 | |
| 4 | 웨이팅 맛집 | 오마카세 | ↓ |
| 5 | 혼밥 | 가성비 맛집 | |
Keywords such as "low price," "zero," and "cost-effectiveness" have surged. The consumer trend of considering both health and economic factors is also reflected in restaurant choices.
Different foods are popular in different regions
Even during the same period, popular food categories varied by region.
Top Keywords in Regional Restaurant Posts
| 지역 | 1위 키워드 | 2위 키워드 | 특징 |
|------|-----------|-----------|------|
| 서울 강남 | 오마카세 | 파인다이닝 | 프리미엄 외식 |
| 서울 성수 | 브런치 카페 | 비건 | MZ 트렌드 |
| 부산 해운대 | 횟집 | 밀면 | 로컬 특화 |
| 제주 | 흑돼지 | 로컬 카페 | 관광 연계 |
| 대전 | 성심당 | 칼국수 | 지역 명물 |
In Seongsu-dong, "vegan" and "brunch" are popular, while in Gangnam, "omakase" still ranks first. This data can be utilized for regional targeted marketing.
Restaurant search patterns by day of the week and time
Upon analyzing the time and content of posts, patterns by day of the week were observed.
Characteristics of Restaurant Posts by Day of the Week
Mon-Wed: "Lunch restaurants," "restaurants for office workers," "cost-effective restaurants"
Thu-Fri: "Dinner gathering places," "group restaurants," "places with a good atmosphere"
Sat-Sun: "Date restaurants," "restaurants with waiting lines," "brunch," "cafes"
The pattern emphasizes cost-effectiveness and accessibility on weekdays, while focusing on ambiance and special experiences on weekends. This can be useful when planning promotions.
Criteria for customers when choosing restaurants
Frequent selection criteria mentioned in restaurant review posts were analyzed.
Top 5 Mentioned Criteria When Choosing Restaurants
| 순위 | 선택 기준 | 언급 비율 | 감정 |
|------|----------|----------|------|
| 1 | 맛 | 89% | 긍정 76% |
| 2 | 가격/가성비 | 67% | 긍정 52% |
| 3 | 분위기/인테리어 | 54% | 긍정 81% |
| 4 | 웨이팅/예약 | 41% | 부정 68% |
| 5 | 주차 | 38% | 부정 72% |
While "taste" naturally ranks first, "cost-effectiveness" was also crucial. Additionally, mentions of "waiting time" and "parking" were mostly negative. Identifying and addressing these issues in advance can improve review scores.
How were these insights obtained?
The data was collected by crawling Naver blogs.
Crawling Settings
Target Channel: Naver Blog
Search Keywords: "restaurants," "restaurant recommendations," etc.
Items Collected: Title, body, date of post, tags, number of likes
Crawling Frequency: Once a week
Example of Collected Data
{
"Channel": "네이버 블로그",
"Title": "성수동 브런치 맛집 추천 - 웨이팅 없이 갈 수 있는 곳",
"Body": "주말에 성수동 갔다가 발견한 브런치 카페예요. 에그베네딕트가 진짜 맛있었고, 인테리어도 예뻐서 사진 찍기 좋았어요...",
"Author": "user_****",
"Post Date": "2025-01-05",
"Tags": ["성수동맛집", "브런치", "카페"],
"Like Count": "234"
}
Example of AI Analysis Results
{
"Post Body": "에그베네딕트가 진짜 맛있었고, 인테리어도 예뻐서 사진 찍기 좋았어요...",
"Sentiment": "Positive",
"Keywords": ["에그베네딕트", "인테리어", "사진"],
"Categories": [
{"category": "Food", "subcategory": "Brunch", "type": "Positive"},
{"category": "Atmosphere", "subcategory": "Interior", "type": "Positive"},
{"category": "Experience", "subcategory": "Photo-worthy", "type": "Positive"}
],
"Location": "성수동",
"Food Type": "브런치/카페"
}
By collecting thousands of posts and automating keyword extraction and sentiment analysis with AI, trends insights like the above can be derived.
Applications of this data
F&B Franchises - New Menu Planning
Planning new menus reflecting the trends of "low price" and "zero." Actual customer-desired keywords can be confirmed through data and incorporated into menus.
Restaurant Marketing - Regional Targeting
By understanding different restaurant trends in each region, tailored marketing messages can be designed.
Food Manufacturers - Market Trend Analysis
Tracking the rise and fall of trend keywords like "mala" and "rose" can inform product planning.
Commercial Area Analysis - Business Preparation
Identifying popular food categories in specific areas and understanding customer responses to competing stores.
Data Integration Methods
The collected data is provided in raw data format, and integration methods can be selected as needed.
Excel Download
Email Sending
API Integration
Direct DB Integration
Summary
Restaurant trends are first seen on Naver blogs. Analyzing crawling data allows for evidence-based decision-making on what foods are trending, regional differences, and what customers consider important.
Hashscraper is responsible for operating, maintaining, and monitoring the crawler. Even in the event of platform policy changes or collection errors, there is no need for direct customer intervention.
Start Now
With Hashscraper, you can automatically collect and analyze restaurant posts on Naver blogs using AI.
Go to Naver Blog Collection Bot
If you need customized keyword settings or AI analysis, please contact us.
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