We extract professor ratings, course-specific reviews, difficulty scores, and university reputation metrics. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Professor Profiles objects from ratemyprofessors.com. All fields typed and schema-versioned.
"professor_id": "228491", "first_name": "John", "last_name": "Smith", "department": "Mathematics", "overall_rating": 4.2, "difficulty_level": 3.8, "would_take_again_pct": 78, "total_ratings": 142
| # | professor_id | first_name | last_name | department | university_id | university_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Student Reviews objects from ratemyprofessors.com. All fields typed and schema-versioned.
"review_id": "R849201", "course_code": "MATH101", "rating": 5.0, "difficulty": 3.0, "attendance_mandatory": false, "review_text": "Great lectures, exams are fair.", "helpful_votes": 12
| # | review_id | professor_id | course_code | date_posted | rating | difficulty |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for University Profiles objects from ratemyprofessors.com. All fields typed and schema-versioned.
"university_id": "U1294", "name": "University of Michigan", "state": "MI", "overall_rating": 4.1, "reputation": 4.5, "food": 3.8
| # | university_id | name | city | state | country | overall_rating |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Department Aggregates objects from ratemyprofessors.com. All fields typed and schema-versioned.
"department_name": "Computer Science", "professor_count": 45, "average_rating": 3.9, "average_difficulty": 4.2, "top_rated_professor_id": "P9921", "total_reviews": 3491
| # | university_id | department_name | professor_count | average_rating | average_difficulty | top_rated_professor_id |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Search & Discovery objects from ratemyprofessors.com. All fields typed and schema-versioned.
"search_query": "physics", "entity_type": "professor", "result_position": 1, "entity_name": "Jane Doe", "rating": 4.8, "scraped_at": "2026-05-12T09:14:33Z"
| # | search_query | entity_type | result_position | entity_id | entity_name | subtitle |
|---|---|---|---|---|---|---|
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Our RateMyProfessors scraper handles GraphQL interception, pagination logic, and rate limits to deliver structured faculty and university data without missing records.
Extract overall ratings, difficulty, and 'Would Take Again' percentages for millions of faculty members.
Capture individual student reviews, grades received, textbook usage, and attendance requirements per course.
Track campus ratings across reputation, internet, food, clubs, and social metrics.
Bypass DOM scraping by intercepting direct GraphQL payloads for cleaner data and lower latency.
Extract standard tags like 'Tough grader' or 'Caring' assigned by students to quantify qualitative feedback.
Monitor upvotes and downvotes on specific reviews to weight sentiment analysis models.
Calculate mean ratings and difficulty scores across specific university departments and faculties.
Paginate through years of historical reviews for longitudinal sentiment analysis.
Run pipelines on daily or weekly cadences to capture new reviews before midterm or final seasons.
Brief in. Clean data out.
Provide university names, department lists, or professor IDs. We define the schema.
We configure Scrapy, GraphQL interception, and proxy rotation for ratemyprofessors.com.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting student sentiment requires navigating dynamic APIs, rate limits, and unstructured user input. Here is our approach.
RateMyProfessors relies heavily on GraphQL. We intercept and decode these API requests directly rather than parsing the DOM, ensuring perfect schema alignment and zero missing fields.
The platform restricts deep pagination on highly reviewed professors. We use targeted date filters and sorting parameters to extract complete historical review sets without hitting hard limits.
Cloudflare and custom rate limiting block aggressive scraping. We route requests through residential proxy pools with randomised delays to maintain high throughput.
Course codes are often entered inconsistently by students (e.g., 'CS101' vs 'CS 101'). We apply regex-based normalisation pipelines to ensure clean joins in your warehouse.
For continuous monitoring, we hash existing reviews and only emit new or modified records, reducing your downstream processing load.
Analyze student sentiment and pain points across disciplines to inform product development.
Monitor department performance and faculty reputation against peer institutions.
Integrate difficulty scores and professor ratings into course scheduling tools.
Use millions of structured student reviews to train education-focused sentiment classifiers.
Correlate university facility ratings like food and internet with housing demand.
Track overall university reputation and happiness scores to predict enrollment trends.
"RateMyProfessors holds the largest unfiltered corpus of student sentiment globally. Extracting it cleanly requires navigating complex GraphQL structures and strict rate limits."
Building a reliable pipeline for RateMyProfessors requires more than basic HTML parsing. The platform relies on dynamic GraphQL queries, aggressive Cloudflare protection, and unstructured user inputs. DataFlirt handles the extraction, normalisation, and infrastructure, delivering clean data directly to your warehouse.
Everything supported by our ratemyprofessors.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass brittle DOM scraping by targeting the underlying GraphQL APIs, ensuring high-speed extraction and perfectly typed data structures.
Requests are distributed across ISP residential proxies to bypass Cloudflare protection and IP-based rate limits without triggering blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ratemyprofessors.com scraping, legality, and pipeline operations.
Ask us directly →Public data extraction is generally permissible. We strictly target public reviews and ratings, avoiding authenticated or private user data.
We utilize residential proxies and realistic TLS fingerprinting to bypass automated bot detection layers.
Yes. We paginate through the entire review history for any given professor or university.
Students enter course codes inconsistently. We apply regex normalisation to standardise formats like 'MATH 101' and 'MATH101'.
A standard university with 2,000 professors can be fully extracted, including all historical reviews, within 4 hours.
Yes. We offer incremental pipelines that run daily or weekly, delivering only new reviews via webhook or S3 diffs.
Yes. All qualitative tags like 'Tough grader' or 'Caring' are extracted as JSON arrays per review and aggregated at the professor level.
20-minute scoping call. Pilot dataset within the week. Production within two. From single department audits to national university sentiment tracking. We build and maintain the pipeline. Tell us your data requirements.
