Why do Airbnb reviews matter differently than hotel OTA reviews?

They're personal and specific. Guests describe the stay like a story, and friction points repeat across stays.

What does Reviato do with Airbnb reviews?

It aggregates reviews, then surfaces aspect trends, sentiment shifts, recurring keywords, and unit-level patterns.

Do you message guests or send auto-replies from Reviato?

No. Reviato doesn't message guests or run any inbox workflow.

Can Reviato remove Airbnb reviews or guarantee Superhost status?

No. It helps you fix the issues that drive negative feedback.

How do I confirm a suspected theme like "check-in confusion"?

Filter by recent dates, search for "check in", "keys", "lockbox", then skim highlights.

Can I compare units or properties side by side?

Yes. Unit-level comparison is the point for multi-unit hosts.

What is sentiment analysis used for?

It helps you spot tone shifts quickly, then validate by opening the reviews behind any spike.

What do word maps and key terms actually do?

They surface repetition so you don't rely on the latest review as "the truth."

What is entity extraction used for in rentals?

It pulls out what guests name directly, amenities, places, or people, so you see what gets mentioned most.

Can I filter by date, rating, and unit?

Yes. Filter by date ranges, rating, and property/unit to isolate what changed.

Do you provide links back to Airbnb listings?

Yes. Keep platform listing links per property for quick cross-checking.

Platforms

Airbnb Review Analytics for Multi-Unit Hosts

Analyze Airbnb reviews for recurring issues like check-in confusion, cleanliness drift, and amenity complaints across units.

How to use this page

Use this page as a practical reference, then apply the ideas to your own operation and constraints.

Focus on one actionable change at a time and measure outcomes before moving to the next change.

Key Highlights

Sentiment & trends: See tone per review, then track it over time. Catch quiet drift before it becomes a pattern.
Rental aspects: Track cleanliness, check-in, communication, location accuracy, value, and amenities.
Unit-level analysis: One unit can drift while your overall rating looks fine. See patterns by property/unit.
Search & filter: Search for a phrase, filter to a unit, narrow to the last two weeks. Validate suspicions fast.

Common Airbnb themes

Cleanliness

Turnover quality and odor mentions.

Check-in

Lockbox, entry codes, and timing.

Communication

Response time and clarity.

Location accuracy

Distance and neighborhood expectations.

Amenities

Wi-Fi, AC, parking, kitchen issues.

Example investigations

Check-in confusion rising

More mentions of "lockbox", "code", "keys", "couldn't get in". Filter last 30 days, search these terms, compare to previous period.

Cleanliness in one unit

One unit has a different cleanliness story. Filter to that unit, search "dirty", "smell", "hair", compare to similar units.

What Reviato extracts from Airbnb reviews

Sentiment

See the tone per review, then track it over time. A portfolio can hold its rating while sentiment quietly drifts.

Aspects

Cleanliness, check-in, communication, location accuracy, value, amenities. Separate "one-off" from "systemic."

Word maps & key terms

Make repetition obvious. If "lockbox" suddenly appears everywhere, something changed.

Entity extraction

What guests name directly, AC, Wi-Fi, parking, metro, "the cleaner", so you track what gets mentioned.

What it is not

Reviato is not a shared inbox for replying to reviews, a messaging platform, or a listings manager.

Catch patterns before they spread

Spot recurring issues early by unit, time window, and theme.

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