IMDB user reviews are the deepest qualitative dataset in entertainment. A single popular movie can carry ten thousand long-form reviews, each with a 1–10 rating and a spoiler flag, going back twenty years. The catch is the page itself: by default it only shows you a paginated, "curated" sort, with no easy way to download the lot. If you're doing pre-release sentiment for a sequel, building a critics-vs-audience gap chart, or pulling a corpus for a film-studies project, the curated view is useless. You want the whole thing in a spreadsheet. This guide walks you through using ExportComments' IMDB Reviews exporter to pull every user review for any title into Excel, CSV, or JSON in one pass.

Why export IMDB reviews

IMDB is unusual on two fronts. First, the rating scale is 1–10, not the 1–5 most other review platforms standardize on. That sounds trivial. It isn't — it's the single most common bug in cross-platform sentiment work, because half the NLP libraries on the open-source shelf assume a 5-star input and silently rescale anything that isn't. Treating an IMDB "7" the way you'd treat a Trustpilot "7" (you can't, there is no Trustpilot 7) is the kind of error that ships into a marketing deck before anyone notices.

Second, IMDB is the home of the review bomb. The Last Jedi in 2017, Captain Marvel in 2019, the Velma series on HBO Max in 2023 — when the audience score collapses overnight, the curated on-site sort smooths over the timestamp pattern that would tell you exactly when and why. Once the data is offline, the bombing is obvious in a histogram. The URL accepts ?sort=submissionDate for chronological order and ?ratingFilter=N to slice by score, but for a full picture you want the entire dataset. Once it's there:

  • Pre-release sentiment for sequels and remakes — mine the original's reviews to find the lines and themes audiences quote, then steer marketing copy toward what the fanbase already loves.
  • Critical-vs-audience gap chart — overlay IMDB's user distribution against Metacritic or Rotten Tomatoes critic scores to find the films where critics and audiences materially disagree.
  • Streaming platform research — bulk-pull reviews for the top 50 titles on a service to study what's resonating in their library.
  • Film-studies and academic bulk pulls — assemble a reviews corpus for a director, a genre, a decade, or a star and run topic models on it.
  • Spoiler-free sentiment scoring — filter out spoiler = true rows before running sentiment, since spoiler reviews are systematically longer and skew negative.
  • Track the long tail of opinion as a film moves from theatrical to streaming to catalog — each window pulls a different audience.

How to export IMDB reviews — step by step

Step 1: Grab the IMDB title URL

Open the title's page on IMDB.com — for example, https://www.imdb.com/title/tt0816692/ for Interstellar. Any canonical title URL works; you don't need to click into the reviews tab first. The tt-prefixed title ID is what the exporter keys off. If you want a specific slice — chronological order, only 10-star reviews, only 1-star reviews — pass the IMDB filtered URL directly (?sort=submissionDate, ?ratingFilter=10) and the exporter respects it.

Step 2: Paste the URL into the exporter

Open the IMDB Reviews exporter and paste the URL into the input field. Got a slate to pull at once? A streaming platform's top 50, a director's filmography, a list of remake-vs-original pairs? Switch to bulk mode and paste one URL per line. Bulk runs return one Excel file per URL, bundled together in a single ZIP at the end of the job, so each title stays cleanly separated.

Step 3: Pick a format

Excel (.xlsx), CSV, or JSON. Excel if you want to pivot, filter, and chart immediately. CSV is the safest pick for BI imports and academic pipelines. JSON if you're piping straight into a notebook or a sentiment model.

Step 4: Start the export

Click Export. The job runs server-side and paginates through IMDB's review feed until it has every public review for that title — spoiler flag, helpful/unhelpful counts, the reviewer's profile URL, the lot. Popular titles with tens of thousands of reviews take a few minutes. Close the tab; the file lands in your dashboard and your inbox when it's ready.

Step 5: Open the file

Open the .xlsx in Excel, Numbers, or Google Sheets. Each row is one review. Columns below.

Inside the export — what fields you get

Each row is a single IMDB user review. You'll find columns for:

  • Reviewer name — the display name shown on the review.
  • Reviewer profile URL — direct link back to the reviewer's IMDB profile, useful for following prolific raters.
  • Rating — the 1–10 score (note: not 1–5 like most other review platforms).
  • Title — the short headline the reviewer wrote.
  • Body — the full review text.
  • Spoiler — true if the reviewer or IMDB tagged the review as containing spoilers.
  • Helpful count — how many other users voted "found this helpful."
  • Unhelpful count — how many voted "not helpful."
  • Created at and Updated at — original timestamp and last-edit timestamp in UTC.

Common workflows

  • Pre-release sentiment for sequels and remakes — export the original film's reviews, sort by helpful_count on the high-rating subset, and pull out the lines and themes audiences keep quoting. That's your marketing-copy bank for the follow-up.
  • Critical-vs-audience gap chart — pivot the rating column into a histogram (1–10 buckets), overlay it against Metacritic or RT critic scores in a chart, and highlight the films where the two distributions materially disagree. Great fodder for trade-press posts and internal marketing decks.
  • Streaming platform research — bulk-pull reviews for a streaming service's top 50 titles and pivot by year, genre, or studio. Surfaces what's quietly carrying the catalog versus what's headline-driven.
  • Film-studies and academia — assemble a reviews corpus for a director, a genre, or a decade, and run topic-modeling or stylometric analysis. The 1–10 rating gives you a built-in label for supervised training — just remember to keep the scale 1–10 in your model and not silently rescale to 1–5, which is the failure mode most off-the-shelf NLP toolkits ship with.
  • Spoiler-free sentiment scoring — filter to spoiler = false before running a sentiment pass. Spoiler reviews are systematically longer and skew negative because they're written by people who needed to vent specifics; including them biases the average.
  • Helpful-low-rating mining — sort by helpful_count within 1–4 star reviews to find the criticism IMDB users have collectively endorsed by upvote — almost always the most consistent grievance about the film. Plot the same dataset on a created-at axis and any review-bombing campaign jumps out as a vertical wall on the timeline.

Plan limits and API access

The Free tier returns up to 100 reviews per export, which is enough to evaluate the format. Personal scales to 5,000 results per export, Premium to 50,000, and Business to 250,000 — enough to capture every review for the deepest catalog titles on IMDB. If you'd rather pull reviews on a schedule or trigger an export from your own pipeline, the same job is available through the REST API and via webhooks. See pricing for the full breakdown.

FAQ

  • Why is the rating 1–10 and not 1–5?
    IMDB has used a 1–10 user rating since launch, and the column reflects that exactly. If you're combining IMDB data with platforms that use 1–5 (Amazon, Walmart, Trustpilot), normalize before averaging — multiply the 1–5 scale by 2, or divide IMDB's 1–10 by 2. Most off-the-shelf sentiment libraries silently assume 1–5, so check before you pipe the column in.
  • Can I export only chronological reviews instead of IMDB's curated sort?
    Yes. Pass IMDB's filtered URL — for example one with ?sort=submissionDate — and the exporter will respect it. ?ratingFilter=N works the same way if you only want a specific score bucket.
  • How do I exclude spoilers before running sentiment?
    Filter the export on the spoiler column and keep only false. Spoiler reviews are systematically longer and skew negative, so leaving them in biases sentiment averages.
  • Does this work for TV shows as well as movies?
    Yes. Any IMDB title page works — features, miniseries, episodic shows. The exporter pulls user reviews on the title page itself; episode-level reviews live on each episode's own page if you need that granularity.
  • Can I follow specific reviewers over time?
    Yes — the reviewer_profile_url column gives you the canonical IMDB profile link, so you can group reviews by reviewer across multiple title exports and study individual rating behavior.
  • What if I have a list of fifty titles to export?
    Use bulk mode: paste one IMDB URL per line and the run returns one file per URL packaged in a single ZIP, so each title's data stays cleanly separated for downstream analysis or for handing off to a research team.