Overview

Every review in Sparrow Intel comes with an analysis layer — sentiment, opinions, language, cleaning and maintenance issues — generated by Chirp AI the moment the review arrives. All of these live on the review detail page (open a review in the inbox and click Analyze), stacked as sections alongside the review body.

This lesson covers what each section means, how to use it, and how it powers the rest of the platform (Predictions, Rules, Tasks).

Why this matters

A review is the single most public signal about your operation. Chirp's analysis makes the insight inside the review readable at a glance — so you spend your time deciding how to respond, not parsing what the guest meant.

Without it, you'd have to re-read every review looking for clues. With it, the clues come pre-tagged.

This is also the same analysis layer that feeds Predictions and that Rules can branch on. The analysis isn't decoration — it's the structured form of every review.

Sentiment

Two sentiment values appear:

• Review sentiment — Chirp's overall read of the whole review, with a confidence score

• Opinion — per-sentence breakdown

Both are color-coded (green for positive, orange for neutral, and red for negative).

The document-level value is what most rules and filters use. The sentence-level breakdown is what helps you catch the things the star rating misses — a 4-star review that buries a real problem in sentence three, or a 2-star review that praises one specific thing you should acknowledge.

A glance at the sentence-by-sentence breakdown takes a couple of seconds and consistently changes how you reply.

Opinions

Opinions are the specific things the guest praised or complained about, extracted by Chirp AI:

"loved the hot tub"

"kitchen was well-equipped"

"WiFi dropped repeatedly"

"felt the cleaning fee was high"

Each opinion shows up as a discrete entry, color-coded by sentiment. Opinions are what your reply should acknowledge — if you see two positive opinions and one negative, your response should address all three (even if the third is a brief acknowledgment that you're looking into it).

This is the most common reply miss: replying to the star rating instead of the opinions. A 5-star review with a small complaint deserves a reply that addresses the complaint, not a generic thank-you.

Cleaning issues and maintenance issues

These are operational, not conversational. Chirp AI extracts them when a review mentions:

• Cleaning issues: "the bathroom wasn't cleaned," "we found hair in the bed," "the kitchen was sticky"

• Maintenance issues: "the dishwasher won't run," "AC was making a clicking noise," "the porch light was out"

Each detected issue becomes an actionable item that you can:

• Acknowledge in your reply

• Convert into a task in Breezeway, Guesty, Hostaway, Track, Asana, or via webhook

• Auto-route via a Rule

We cover the task creation flow in Rules for Reviews & Predictions. The point here is just: those issues are already extracted for you, so you don't have to spot them by hand.

Language detection

Every review shows the detected original language. Non-English reviews carry an English translation toggle so your team can read them, and the reply composer defaults to the original language.

If the detection is wrong (rare), you can override the language in the composer when you reply.

How the analysis drives the rest of the platform

Everything in the analysis layer is rule-eligible:

• A rule can auto-respond when sentiment is positive AND no cleaning or maintenance issues are detected

• A rule can create a task whenever a maintenance issue is detected on any review

• A rule can mark for removal any review whose body matches a policy-violation keyword

• A rule can email a supervisor whenever a 1-star review posts on Airbnb

It's also the foundation of Predictions — Chirp's per-checkout prediction of what a review will look like uses the same opinion-and-issue extraction model on the conversation history that this analysis layer uses on the review itself.

The same analysis data that helps you reply better also feeds the system that warns you before bad reviews post.

What if the analysis got something wrong?

Chirp is good but not infallible. Occasional misclassification happens, especially on short or sarcastic reviews.

If you notice a pattern (e.g., a particular phrase consistently misclassified), let our support team know via the in-product chat. We use real reviews to tune the model.

In the meantime, you can:

• Edit the AI reply suggestion to reflect what you actually see

• Override the language in the composer

• Star the review and circle back when the pattern is clearer

Up next

You can read every review in seconds now. The last Cruising Altitude lesson — Tracking Review Removals — covers what to do when a review shouldn't be there at all.