> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gladeapi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Fetch and Analyze Amazon Product Reviews with Glade API

> Fetch, filter, and analyze Amazon product reviews using Glade API. Learn how to paginate reviews, filter by star rating, and search review text at scale.

Amazon product reviews are a rich source of customer feedback, competitive intelligence, and product development insight. Glade API's reviews endpoint gives you paginated access to review data with filtering and search — no scraping required.

## Fetching product reviews

`GET /api/amazon/product/reviews` returns structured review data for any ASIN. Pass the ASIN and domain to get started:

```bash theme={null}
curl "https://gladeapi.com/api/amazon/product/reviews?asin=B0D1XD1ZV3&domain=US&page=1" \
  -H "API-KEY: YOUR_GLADE_API_KEY"
```

**Supported query parameters:**

| Parameter | Type | Description |
| - | - | - |
| `asin` | string | The product ASIN |
| `domain` | string | Marketplace (e.g. `US`, `UK`, `DE`) |
| `page` | integer | Page number for paginated results |
| `rating` | string | Filter by star rating (see below) |
| `onlyVerifiedReviews` | boolean | When `true`, returns only verified purchase reviews |
| `search` | string | Keyword search within review text |

The response contains two review lists under `data.amazonProduct`:

* **`topReviews`** — Featured reviews when the data source provides them.
* **`reviewsPaginated.reviews[]`** — the full paginated review set, subject to your filters.

Each review object includes the following fields:

| Field | Description |
| - | - |
| `id` | Unique review identifier |
| `title` | Review headline |
| `body` | Full review text |
| `imageUrls[]` | Customer-uploaded images |
| `videos[]` | Customer-uploaded video clips |
| `rating` | Star rating (1–5) |
| `helpfulVotes` | Number of users who found the review helpful |
| `verifiedPurchase` | Whether Amazon verified the reviewer bought the product |
| `reviewer.id` | Anonymous reviewer identifier |
| `reviewer.name` | Display name |
| `reviewer.url` | Link to the reviewer's Amazon profile |

## Filtering reviews

Glade API's filtering parameters let you narrow the review set before it reaches your application, saving both processing time and API units.

**Filter by star rating** using the `rating` parameter:

| Value | Matches |
| - | - |
| `ALL` | All ratings (default) |
| `FIVE_STAR` | 5-star reviews only |
| `FOUR_STAR` | 4-star reviews only |
| `THREE_STAR` | 3-star reviews only |
| `TWO_STAR` | 2-star reviews only |
| `ONE_STAR` | 1-star reviews only |

**Filter to verified purchases only** by adding `onlyVerifiedReviews=true`. Verified reviews carry more signal because Amazon has confirmed the reviewer actually bought the product.

**Search review text** using the `search` parameter to find mentions of specific features, problems, or topics.

The following example fetches 1-star verified reviews that mention the word "defective" — useful for monitoring quality issues on your own products or a competitor's:

```bash theme={null}
curl "https://gladeapi.com/api/amazon/product/reviews?asin=B0D1XD1ZV3&domain=US&rating=ONE_STAR&onlyVerifiedReviews=true&search=defective" \
  -H "API-KEY: YOUR_GLADE_API_KEY"
```

You can combine all three filters simultaneously. The filters are applied server-side before the response is returned, so you receive only the reviews that match all conditions.

## Paginating through all reviews

For popular products with thousands of reviews, you'll need to iterate through multiple pages to collect the full data set. The `reviewsPaginated.pageInfo` object tells you where you are and whether more pages exist:

| Field | Description |
| - | - |
| `currentPage` | The page number just returned |
| `totalPages` | Total number of pages available |
| `totalResults` | Total number of reviews matching your filters |
| `hasNextPage` | `true` if there is a page after the current one |
| `hasPrevPage` | `true` if there is a page before the current one |

The following Python function iterates all pages and returns a flat list of every review:

```python theme={null}
import os, requests

GLADE_KEY = os.environ['GLADE_API_KEY']

def get_all_reviews(asin: str, domain: str = 'US') -> list:
    all_reviews = []
    page = 1
    while True:
        resp = requests.get(
            'https://gladeapi.com/api/amazon/product/reviews',
            params={'asin': asin, 'domain': domain, 'page': page},
            headers={'API-KEY': GLADE_KEY},
        )
        data = resp.json()['data']['amazonProduct']
        paginated = data.get('reviewsPaginated', {})
        reviews = paginated.get('reviews', [])
        all_reviews.extend(reviews)
        if not paginated.get('pageInfo', {}).get('hasNextPage'):
            break
        page += 1
    return all_reviews
```

<Warning>
  Fetching every review page for a product with thousands of reviews consumes one unit per page. Set a page limit or use the `search` filter to narrow results first.
</Warning>

For most analytical use cases, consider capping collection at a fixed number of pages or using the `search` filter to focus on the topics you care about.

## Use cases

<CardGroup cols={2}>
  <Card title="Sentiment analysis" icon="face-smile">
    Collect reviews at scale, run NLP over the `body` field, and surface recurring pain points. Use `rating` filters to compare language patterns between satisfied and dissatisfied customers.
  </Card>

  <Card title="Feature requests" icon="lightbulb">
    Search for words like `"wish"`, `"if only"`, or `"missing"` to surface unmet customer needs. Review text is often more candid about feature gaps than any survey.
  </Card>

  <Card title="Competitive intelligence" icon="magnifying-glass-chart">
    Compare `rating` distributions and review themes across competing ASINs in the same category. Products with lower average ratings in specific areas reveal exploitable weaknesses.
  </Card>

  <Card title="Quality monitoring" icon="triangle-exclamation">
    Set up a scheduled job that polls 1-star verified reviews for your own products. Alert when one-star volume spikes, giving you an early warning before ratings visibly degrade.
  </Card>
</CardGroup>


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