Sentiment analysis of Google reviews: a practical guide

Why analyse Google review sentiment

Sentiment analysis of Google reviews matters for any business with physical locations, because Google Maps is where many customers write about their visit, in their own words, for other customers to read. The star rating summarises satisfaction; the text explains it. Analysing the text by topic turns thousands of reviews into a view of which parts of the visit are working and which are not, store by store.

Collecting the reviews

• Every location's reviews, mapped to the right store.
• The review date, rating and full text, and whether the business replied.
• Regular refreshes, so new reviews and edits are included.

Google allows reviewers to edit their reviews, and the review date updates when they do, so treat an edited review as a new data point rather than a duplicate.

Handling ratings and text

• Rating-only reviews count towards volume and the average rating, but have no text to analyse. Report how many there are.
• Very short texts ("good", "nice") carry little or no topic. Mark them unclear.
• Detailed reviews often mix praise and complaint. Measure sentiment for each topic separately.
• Ratings and text sometimes disagree. Trust the text for topic sentiment; keep the rating for the overall score.

Topics that matter in Google reviews

Store reviews on Google tend to cover a recognisable set of topics. Build your own list from your reviews, but expect most of these:

• Staff: friendliness, knowledge, attentiveness.
• Waiting: queues, billing, service speed.
• Product: range, quality, availability.
• Price and value.
• Store: cleanliness, layout, comfort, parking, accessibility.
• Policies: returns, exchanges, warranties.
• Location-specific: mall access, opening hours, signage.

Measuring by topic and store

• Negative share for each topic: negative mentions divided by mentions with known sentiment.
• The same, per store, over a recent window such as 90 days.
• The change against the previous window.
• The mention count next to every number.

Reading the results

• A topic rising across many stores at once points to a central cause: a policy, a product, a system.
• A topic rising at one store points to something local: staffing, management, a refit.
• A topic with high negative share but steady over years may be structural, such as parking at a mall; reply well and decide whether it is worth fixing.
• A strength whose positive share is falling is an early warning worth reading.

Pitfalls

• Using one sentiment score per review, which hides mixed reviews.
• Averaging stores with very different review counts.
• Ignoring language. In India, many Google reviews mix English with Hindi or a regional language.
• Forgetting that Google may remove reviews for policy reasons; a sudden drop in volume is not always a change in customers.

Common questions

A worked example

A jewellery chain analyses 90 days of Google reviews across 60 stores. Staff and product are strongly positive everywhere. The share of negative mentions about billing is 9% across the network but 22% at eight mall stores, up from 11% the previous quarter, on about 600 mentions. Reading the reviews shows the cause: a new billing step for gold-rate confirmation that takes longer at busy times. The fix goes to the operations owner, not the eight store managers.

Checklist

• Every store's reviews collected and mapped to the right location.
• Rating-only reviews counted but kept out of topic sentiment.
• Topic negative share per store over a recent window.
• Changes compared with the previous window.
• Reviews read before any action.

Doing this with Falcon

Falcon measures sentiment for every theme a review mentions, not one score per review. Each mention keeps the exact words that support its label, negative share is reported by topic and month with small samples flagged, and unclear texts are kept visible rather than forced into a category. Every number opens the reviews behind it.