IMDb Reviews Scraper — Extract Movie & TV Show User Reviews

Scrape IMDb user reviews for any movie or TV show — full review text, author ratings, dates, helpful votes, and spoiler flags. No coding required.

Paste IMDb title URLs (or just type movie/show names), click Start, and get one clean JSON record per review: review title, full text, the author's rating out of 10, author nickname and profile ID, submission date, helpful up/down votes, and the spoiler flag. Sort by helpfulness, newest, oldest, rating, or votes, and optionally filter out spoiler reviews server-side — filtered reviews are never billed.

Titles can be given as any imdb.com/title/tt… URL, or simply by name — "Breaking Bad" resolves to the right title automatically via IMDb search (never billed). Raise Max Titles per Name to scrape a whole franchise's reviews from one query.

All this at $1 per 1,000 reviews — 5× cheaper than comparable scrapers, with no start fee and no monthly rental.

Rated Excellent — 4.6 based on 25 reviews on Trustpilot.

Output example: IMDb review data in JSON

Each review becomes one dataset item:

{
  "title_id": "tt4154796",
  "title_name": "Avengers: Endgame",
  "title_url": "https://www.imdb.com/title/tt4154796/",
  "query": "Avengers Endgame",
  "id": "rw11203827",
  "title": "It's Been a Long, Long Time",
  "text": "This was the conclusion of a 10-year story, and they did such a great job during the last hour of the movie that one can almost forget about some of the aspects of the movie that just don't make sense...",
  "rating": 8,
  "author": { "id": "ur96346298", "nickname": "sangomiguel" },
  "date": "2026-02-11",
  "is_spoiler": true,
  "helpful_votes": { "up": 15, "down": 2 }
}

You can download the dataset as JSON, CSV, Excel, XML, or an HTML table.

What data does the IMDb Reviews Scraper extract?

Data group	Fields
Review	Review title, full text, author rating out of 10, submission date, review ID
Author	Nickname and IMDb profile ID
Engagement	Helpful votes up and down, spoiler flag
Title context	Title tt-id, name, IMDb link, and your original query — every row is self-contained

How to scrape IMDb reviews

1. Click Try for free.
2. Paste IMDb title URLs and/or type movie or show names — names resolve to titles automatically.
3. Pick a sort order (most helpful by default), set Max Reviews per Title, and optionally exclude spoilers.
4. Click Start and wait for the run to finish.
5. Open the Dataset tab to preview your data, then download it as JSON, CSV, Excel, XML, or an HTML table.

No proxies, no browser setup, no CAPTCHA solving.

Input

{
  "startUrls": ["https://www.imdb.com/title/tt4154796/"],
  "searchQueries": ["Breaking Bad"],
  "maxTitlesPerSearch": 1,
  "maxReviewsPerTitle": 100,
  "sortBy": "helpfulness",
  "includeSpoilers": true,
  "country": "US"
}

Field	Type	Description
startUrls	array	IMDb title page URLs (any imdb.com/title/tt… URL, review pages included).
searchQueries	array	Movie/show names — each resolves via IMDb search to its top match(es), never billed.
maxTitlesPerSearch	integer	Search matches to scrape per name (default 1).
maxReviewsPerTitle	integer	Max reviews per title (default 100, 25 per page). Controls your cost.
sortBy	string	helpfulness (default), newest, oldest, rating, votes.
includeSpoilers	boolean	Off = spoiler-flagged reviews are skipped server-side and never billed.
country	string	2-letter code localizing regional title names (default US).

How much does it cost to scrape IMDb reviews?

Scraping IMDb reviews costs $1 per 1,000 reviews ($0.001 per review) — 5× cheaper than comparable scrapers (which charge ~$5 per 1,000 plus up to a $0.09 fee every run), with no Actor start fee. Title discovery is free: searches and URL resolution are never billed.

Apify gives you $5 in free credits every month, so you can scrape your first 5,000 reviews free and test the IMDb Reviews Scraper before paying anything.

FAQs

Why scrape IMDb reviews?

• Sentiment analysis — thousands of dated, rated review texts per title, ready for NLP.
• Audience research — see what viewers praise and criticize, sorted by helpfulness so the most representative opinions come first.
• Reception tracking — scrape newest-first on a schedule to follow how opinion shifts after release.
• Datasets for ML — clean JSON with ratings, votes, and spoiler labels.

Can I scrape reviews for several titles in one run?

Yes — add as many URLs and names as you like. Every row carries the title's id, name, and link plus your original query, so one dataset with many titles stays easy to split.

Will I be charged twice if the same title appears twice?

No. Titles are deduplicated per run — the same title arriving via a URL and a name search is scraped once. Every review row is billed exactly once, filtered-out spoiler reviews are never billed, and failed pages are never billed.

How many reviews can I get per title?

As many as IMDb serves — popular titles have thousands (Avengers: Endgame has ~10,000). Set Max Reviews per Title to cap the spend per title.

Is it legal to scrape IMDb reviews?

The Actor extracts publicly available reviews only — visible to any visitor, no login involved. Author nicknames are public by the reviewer's choice. You are responsible for how you use the data; consult a lawyer if unsure about your use case.

Need title data too?

Use our IMDb Scraper — search 11M+ titles with filters, scrape the Top 250 and box-office charts, and pull full title profiles with cast and box office, from $1 per 1,000 titles.

Support

Built by developers, for developers — when you reach out, you talk to the engineers who built the Actor, not a support script. Message us anytime and we'll resolve your query within 1 working day.

Email: happy.to.help@omkar.cloud

Love It? Star It! ⭐

From one business owner to another: if this Actor saved you hours of copying reviews off IMDb, please rate the IMDb Reviews Scraper on Apify.

Here's why it matters: most people judge an Actor by its reviews before trying it. Your rating helps the next person hit "Try for free" with confidence, and it helps the Actor rank higher in the Apify Store.

It takes only 1 second, and means the world to me.