CyberMax-tools/app-complaint-stats

Gripeline: App Store & Google Play complaint stats

Need it fresh, filtered or via API? This free file is a snapshot (one snapshot of app review complaints), last updated 2026-09-25.

• Gripeline on Apify ($0.0001 per review): pulls fresh reviews and complaint summaries for any app, with new-review monitoring.
• Get an email when this dataset updates: free, double opt-in, unsubscribe any time.

What are users of popular apps complaining about right now? One row per app per store per snapshot, computed from the newest reviews (up to 300 per app and store, US App Store and English Google Play):

• star mix (1 to 5), average rating of the sample, share of negative (1-2 stars) and positive (4-5 stars) reviews, developer reply rate;
• top complaint phrases and top praise phrases with the number of reviews that mention each;
• worst versions: app versions with the lowest average rating in the sample;
• the store's overall rating and rating count, for contrast with recent sentiment.

Aggregates only, on purpose: this dataset contains no review text, no review titles and no reviewer names.

First snapshot (2026-09-23): 30 popular apps, 48 app/store rows.

Use it

from datasets import load_dataset
df = load_dataset("CyberMax-tools/app-complaint-stats", split="train").to_pandas()
print(df.sort_values("negative_share_pct", ascending=False)[["app", "store", "negative_share_pct", "store_rating"]].head(10))
print(df.loc[df.app == "Instagram", "top_complaints"].iloc[0])

Files

data/snapshot-YYYY-MM-DD.parquet (one file per refresh, one train split; filter by snapshot_date for trends) and the same rows as .jsonl.

column	meaning
snapshot_date	date of the run (UTC)
store	app_store (US) or google_play (English)
app_id, app, developer, category, store_url	the app
store_rating, store_rating_count	the store's all-time rating and count
sample_reviews, sample_from, sample_to	size and date range of the newest-reviews sample
sample_avg_rating, stars_1 ... stars_5	star mix of the sample
negative_share_pct, positive_share_pct	% of the sample at 1-2 and 4-5 stars
developer_reply_rate_pct	% of sampled reviews with a developer reply (Google Play; null where the store doesn't expose it)
top_complaints, top_praise	{phrase, reviews} pairs: phrases most common in negative / positive reviews, with the number of reviews containing each
worst_versions	{version, reviews, avg_rating, negative_share} for the lowest-rated versions
summary	one-line plain-English verdict

Get fresh data

• Any app, any country, full review rows or summaries (JSON for AI agents, MCP-callable): Gripeline on the Apify Store.
• Browse this snapshot in your browser: free Gripeline Space.

Notes

The newest 300 reviews skew toward whatever happened this week (an outage, a policy change), which is the point: it is a recent-sentiment signal, not an all-time rating. Phrases are the most frequent two-word phrases (then single words) in the sample's negative or positive reviews, counted once per review, so some are generic. Not affiliated with Apple, Google or any app listed; app names are trademarks of their owners.

License: CC BY 4.0 (our derived statistics). Built by CyberMax.

More free from CyberMax

• All free CyberMax demos & datasets
• Gripeline: What App Users Complain About
• Swellmeter: Rising Google Searches in AI & Tech

Quickstart notebook

Load this dataset with pandas and see real outputs in the quickstart notebook. Need it refreshed for your own list? Gripeline — App Store & Google Play Reviews Scraper, Complaints.