Name What Gets Measured
Give AllyHub a product URL or ASIN, add whoever you want measured alongside it, and say if you only want one slice of the reviews.
Two products, the same 4.4 stars, and one of them is quietly in trouble — put sentiment on a scale that tells them apart.
From one product link — or a shortlist of rivals — to a scored Amazon review sentiment breakdown in three steps.
Give AllyHub a product URL or ASIN, add whoever you want measured alongside it, and say if you only want one slice of the reviews.
Each review the listing shows is classed positive, neutral, or negative, given an emotion label, and counted into the split — per product, and per slice if you named one.
Read the split, export it, or drop it into your report. Save it as a Playbook and the same products get re-scored on your schedule.
A single run goes in this order: what gets measured, what sits under each score, who it sits beside, how to narrow the read, where it works, and how the numbers come out.
Every review that listing puts on the page is read and classed positive, neutral, or negative, and the split comes back as three percentages instead of one average.
Negative covers both a shrug and a grudge. AllyHub labels the emotion behind each review — frustration, delight, regret, relief — and reports how much of the total each one accounts for.
Feed in a shortlist of ASINs and each one is measured on the same scale, so the distance between you and a rival is a figure rather than an impression.
Say up front which part you care about — just the low-star reviews, or just the ones that mention fit — and only that part of what the listing shows is collected and scored.
Score a listing on amazon.co.uk, .de, or .co.jp exactly as you would on .com, and have the result written back in whichever language your report uses.
The scores come out as JSON or CSV, or go straight down the API into whatever you report in. Nothing here runs on Amazon API access, and nothing asks you to write code.
Sellers, product teams, researchers, and agencies who need opinion measured rather than recalled.
The pain: you and the rival you actually worry about both sit at 4.4 stars, so the number everyone quotes tells you nothing about who is winning. Measured properly, the two stop looking alike — one of you is carrying frustration the other isn't, and that becomes a figure you can act on instead of a suspicion you keep having.
The pain: the factory changed something in March and nobody can say whether buyers noticed. Score the reviews now, keep the run, and score them again once the next batch has been shipping a while — ask for the difference and what you take to the supplier is a movement, not a hunch.
The pain: the category benchmark someone asked you for currently exists as twelve browser tabs of star averages, and you cannot show your work. Hand over the shortlist and what comes back is a table with a method behind it — one that still holds when the room asks where the number came from.
The pain: last month's report said sentiment improved, and the evidence was your impression from reading a few reviews. Re-score the client's listings on the cadence you report on, ask for the comparison, and the figure you show up with has last month's printed beside it.
Explore more AI-powered tools across research, content, and data.

Turn thousands of Amazon reviews into a clear read. AllyHub's Amazon review summarizer surfaces top pros, cons, and buyer objections. Try free.

Scrape Amazon product reviews at scale. AllyHub extracts ratings and review text from any ASIN for competitive research and sentiment analysis. Try free.

Research Amazon products with AI. AllyHub analyzes demand, competition, and profit potential across any category to surface winning products. Try free.
Guides on ASIN research, variation mapping, and bulk product data workflows.

Struggling to scrape Amazon product data without getting blocked? Learn safe, effective Amazon scraper methods using APIs, no-code tools, and Python.

Discover the 10 best Amazon competitor analysis tools used to track competitors, uncover keyword gaps, and understand why top listings outperform yours.

Discover the best Amazon SEO tools to boost your rankings, find high-converting keywords, and outpace competitors. Reviewed and ranked for e-commerce marketers.
Questions that come up when opinion has to become a number someone else will read.
Amazon sentiment analysis is the practice of measuring what a listing's reviews say about how buyers feel, rather than reading a star average as though it were the same thing. It is an NLP term of art, which is why the phrase turns up more often in research papers than in seller tools. AllyHub's version is built for the practical end of it: a measurement meant to be set against another one.
As many as the listing puts on the page. There is no Show More button to press and no page two to walk through, so the batch on screen is the batch that gets counted — and that batch is the denominator behind every percentage you get back. It is the same set you would skim yourself, counted properly instead.
Yes. Scoring a single product costs nothing on the free plan. Comparison runs, re-scoring on a schedule, and piping the figures into your own reporting are what the paid plans cover.
It reads the sentence rather than a keyword list — negation, comparison, and "works fine, but" all change the label. Sarcasm and reviews that praise one thing while damning another are where every scorer is weakest, AllyHub included. Treat a small gap as noise and act on the wide ones.
Different jobs. The Amazon Review Summarizer is for reading — ranked pros and cons, quotes you can check, one product you are deciding about. This page is for measuring: numbers you can put in a report, defend when someone questions them, and produce again next month. Sellers tend to end up running both.
Most of them score a product, hand you a percentage and a word cloud, and charge the same to do it again next month. AllyHub keeps the scoring criteria and the page structure it worked out, so repeat runs over the same shortlist get faster every time and cost less. It compounds with every task instead of restarting.