# Home Depot Reviews Scraper (`wibuild.in/home-depot-reviews-scraper`) Actor

Scrape Home Depot reviews, ratings, photos, and seller responses from any Home Depot US product page — clean JSON ready for sentiment analysis, competitor research, and e-commerce analytics.

- **URL**: https://apify.com/wibuild.in/home-depot-reviews-scraper.md
- **Developed by:** [Wibuild](https://apify.com/wibuild.in) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Home Depot Product Reviews Scraper | Extract Customer Ratings & Feedback (US)

**Scrape Home Depot reviews**, ratings, photos, and seller responses from any **Home Depot US product page** — clean, analysis-ready data for sentiment analysis, competitor research, and e-commerce analytics.

Paste a product URL → choose **Max Mode** (maximize unique coverage) or filters → get structured results you can export as JSON, CSV, or Excel.

***

### 60-second overview

| Goal | What to set |
|---|---|
| **Max coverage** | `maxMode = true` |
| **Quick sample** (e.g. latest 30) | `maxMode = false`, sort `newest`, `maxreviews = 30` |
| **Only 1★ complaints** | `maxMode = false`, `starRatings = ["1"]` |
| **Broad coverage without Max Mode** | Select **all sorts** × **star ratings 1–5** (see below) |

***

### Why use this Home Depot review scraper?

- **Max Mode** — maximizes unique coverage; often the full set for typical products. High-volume SKUs may be partial due to site limits.
- **Rich fields** — stars, review text, photos, verified purchase, incentivized flags, seller replies, helpful votes, and rating breakdowns.
- **Analysis-ready output** — product stats (`Average_Rating`, `Total_Reviews`, star counts) included on every row.
- **Flexible filters** — newest / oldest / photos / most helpful / highest / lowest × 1–5 stars.
- **Easy exports** — run on Apify, push to Dataset, download CSV/Excel/JSON for dashboards (Power BI, Looker, Sheets) or Python.

Ideal for: **product managers**, **brand analysts**, **market research**, **VOC / voice-of-customer**, **assortment teams**, and **AI/NLP pipelines**.

***

### Max Mode — maximize unique coverage

Turn **`maxMode` ON** when you want the broadest unique review set for a product.

**Max Mode maximizes unique coverage** — often the **full set for typical products**. **High-volume SKUs may be partial due to site limits.**

While Max Mode is ON, **sort and star filters are ignored** — the actor focuses on maximizing coverage automatically.

```json
{
  "URL": "https://www.homedepot.com/p/PRODUCT-NAME/317525930",
  "maxMode": true,
  "maxreviews": 5000
}
```

Tip: keep `maxreviews` high (e.g. 5000) so the run does not stop early.

***

### Filters (Max Mode OFF)

When **`maxMode = false`**, you control exactly what is collected using **sort × star** combinations.

#### Available sorts

| Input value | Meaning |
|---|---|
| `newest` | Most recent reviews |
| `oldest` | Oldest first |
| `photoreview` | Reviews with photos |
| `mosthelpfull` | Most helpful |
| `highestrating` | Highest star first |
| `lowestrating` | Lowest star first |

#### Star ratings

`all` · `5` · `4` · `3` · `2` · `1`

#### Examples

**Latest reviews only:**

```json
{
  "URL": "https://www.homedepot.com/p/.../317525930",
  "maxMode": false,
  "sorts": ["newest"],
  "starRatings": ["all"],
  "maxreviews": 100
}
```

**Only negative feedback (1–2 stars):**

```json
{
  "URL": "https://www.homedepot.com/p/.../317525930",
  "maxMode": false,
  "sorts": ["newest"],
  "starRatings": ["1", "2"],
  "maxreviews": 2000
}
```

**Photo reviews + most helpful:**

```json
{
  "URL": "https://www.homedepot.com/p/.../317525930",
  "maxMode": false,
  "sorts": ["photoreview", "mosthelpfull"],
  "starRatings": ["all"],
  "maxreviews": 2000
}
```

***

### Get broad / near-complete coverage **without** Max Mode

You can still aim for wide coverage with filters alone:

1. Set `maxMode` to **false**
2. Select **all 6 sorts**
3. Select stars **`5`, `4`, `3`, `2`, `1`** (or `all`)
4. Set `maxreviews` high enough

```json
{
  "URL": "https://www.homedepot.com/p/.../317525930",
  "maxMode": false,
  "sorts": ["newest", "oldest", "photoreview", "mosthelpfull", "highestrating", "lowestrating"],
  "starRatings": ["5", "4", "3", "2", "1"],
  "maxreviews": 5000
}
```

Duplicates are removed automatically. For the broadest pull with the least setup, **Max Mode is the simplest choice**.

| Approach | Best for |
|---|---|
| **Max Mode ON** | Maximize unique coverage / VOC dumps / model training |
| **Filters (Max Mode OFF)** | Targeted pulls — complaints, photos, latest only |

***

### Input reference

| Field | Required | Description |
|---|---|---|
| `URL` | Yes | Home Depot **US** product URL (must include the numeric item id) |
| `maxMode` | No (default `true`) | `true` = maximize unique coverage; `false` = use sort/star filters |
| `sorts` | No | Multi-select; used only when `maxMode` is false |
| `starRatings` | No | `all` or `1`–`5`; used only when `maxMode` is false |
| `maxreviews` | No | Maximum unique reviews to keep (e.g. `30` for a quick test) |

***

### Output — sample review object

Each dataset item is one review, with review details and product-level stats:

```json
{
  "Review_ID": "400190853",
  "Title": "Bring my Bagged Wind Tunnel Back!!",
  "ReviewText": "Please bring back the bagged Windtunnel!...",
  "Rating": 1,
  "SubmissionDate": "2026-08-11",
  "LastModificationDate": "2026-08-19",
  "UserNickname": "Neen",
  "UserLocation": "Fort Myers FL",
  "AuthorId": "zsq5y832cofhg9tswr7u0e5kvm",
  "ProductId": "317525930",
  "Item_ID": "317525930",
  "Product_URL": "https://www.homedepot.com/p/.../317525930",
  "IsRecommended": null,
  "IsRatingsOnly": false,
  "IsFeatured": false,
  "IsSyndicated": true,
  "SourceClient": "hoover-us",
  "ContentLocale": "en_US",
  "Helpfulness": 0,
  "TotalPositiveFeedbackCount": 0,
  "TotalNegativeFeedbackCount": 0,
  "TotalFeedbackCount": 0,
  "BadgesOrder": ["verifiedPurchaser"],
  "IsVerifiedPurchaser": true,
  "IncentivizedReview": false,
  "IncentivizedReview_Label": null,
  "PhotoCount": 1,
  "PhotoUrls": ["https://example.com/photo.jpg"],
  "HasSellerResponse": true,
  "SellerResponse_Department": "Hoover Customer Support",
  "SellerResponse_Date": "2026-08-19",
  "SellerResponse_Text": "Thank you for taking the time to share your feedback...",
  "Review_Type": "with_review_text",
  "With_Review": "Yes",
  "Sort": "newest",
  "Scrape_Pass": "newest_all_stars",
  "Total_Reviews": 1348,
  "Average_Rating": 4.21,
  "Product_RatingsOnlyReviewCount": 506,
  "Product_RecommendedCount": 980,
  "Product_NotRecommendedCount": 120,
  "Product_5_Star_Count": 430,
  "Product_4_Star_Count": 172,
  "Product_3_Star_Count": 78,
  "Product_2_Star_Count": 64,
  "Product_1_Star_Count": 89
}
```

Download from the Apify Dataset as **JSON / CSV / Excel**.

***

### What analysis can you run on this data?

#### 1. Rating & quality monitoring

- Track `Average_Rating` and star mix (`Product_1_Star_Count` … `Product_5_Star_Count`)
- Spot rating changes after product updates or new listings

#### 2. Sentiment & text mining

- Run sentiment on `ReviewText` / `Title`
- Find common complaints vs praise themes
- Compare language in 1–2★ vs 4–5★ reviews

#### 3. Trust & authenticity signals

- Filter `IsVerifiedPurchaser`, `IncentivizedReview`, `IsSyndicated`
- Separate rating-only rows (`Review_Type`) from written reviews

#### 4. Photo & UGC research

- Use `PhotoCount` / `PhotoUrls` for visual customer content

#### 5. Brand response quality

- Measure reply rate with `HasSellerResponse`
- Compare `SubmissionDate` vs `SellerResponse_Date`

#### 6. Competitor & assortment research

- Run multiple Home Depot product URLs
- Benchmark volume, average rating, and negative-review share across brands

#### 7. Trends over time

- Chart review volume by `SubmissionDate`
- Detect launch spikes or seasonal patterns

***

### Tips

- Prefer **Max Mode** when you want maximum unique coverage (full set for typical products; high-volume SKUs may be partial due to site limits).
- Use **filters + a small `maxreviews`** for cheap tests or “latest only” dashboards.
- One URL = one product; run multiple times (or a workflow) for category research.
- Some fields may be empty when the site does not provide them (e.g. location, recommend flag) — that is normal.

***

### Keywords

Home Depot reviews scraper · Home Depot product reviews · scrape Home Depot ratings · Home Depot customer feedback · extract Home Depot review text · Home Depot sentiment analysis data · e-commerce review scraping Apify · VOC competitor analysis · Home Depot UGC photos · verified purchase reviews export · Home Depot star ratings dataset

***

*Built for teams who need reliable Home Depot US review data — without manual copy-paste.*

# Actor input Schema

## `URL` (type: `string`):

Home Depot US product page URL (must contain the numeric item id).

## `maxMode` (type: `boolean`):

ON = maximize unique coverage (ignores Sort and Star filters). Often the full set for typical products; high-volume SKUs may be partial due to site limits. OFF = use only the filters you select below.

## `sorts` (type: `array`):

One or more sorts. Combined with Star ratings as a cross-product. Empty = newest only.

## `starRatings` (type: `array`):

Empty or \["all"] = all stars. Otherwise pick 1–5. Crossed with each selected sort.

## `maxreviews` (type: `integer`):

Stop after this many unique reviews. Use a small number (e.g. 30) for a quick sample.

## Actor input object example

```json
{
  "URL": "https://www.homedepot.com/p/HOOVER-WindTunnel-Bagless-Pet-Upright-Vacuum-Cleaner-with-Automatic-Cord-Rewind-For-Carpet-Hardwood-and-Upholstery-UH71320V/317525930",
  "maxMode": true,
  "sorts": [
    "newest"
  ],
  "starRatings": [
    "all"
  ],
  "maxreviews": 5000
}
```

# Actor output Schema

## `reviews` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "URL": "https://www.homedepot.com/p/HOOVER-WindTunnel-Bagless-Pet-Upright-Vacuum-Cleaner-with-Automatic-Cord-Rewind-For-Carpet-Hardwood-and-Upholstery-UH71320V/317525930",
    "sorts": [
        "newest"
    ],
    "starRatings": [
        "all"
    ],
    "maxreviews": 5000
};

// Run the Actor and wait for it to finish
const run = await client.actor("wibuild.in/home-depot-reviews-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "URL": "https://www.homedepot.com/p/HOOVER-WindTunnel-Bagless-Pet-Upright-Vacuum-Cleaner-with-Automatic-Cord-Rewind-For-Carpet-Hardwood-and-Upholstery-UH71320V/317525930",
    "sorts": ["newest"],
    "starRatings": ["all"],
    "maxreviews": 5000,
}

# Run the Actor and wait for it to finish
run = client.actor("wibuild.in/home-depot-reviews-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "URL": "https://www.homedepot.com/p/HOOVER-WindTunnel-Bagless-Pet-Upright-Vacuum-Cleaner-with-Automatic-Cord-Rewind-For-Carpet-Hardwood-and-Upholstery-UH71320V/317525930",
  "sorts": [
    "newest"
  ],
  "starRatings": [
    "all"
  ],
  "maxreviews": 5000
}' |
apify call wibuild.in/home-depot-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wibuild.in/home-depot-reviews-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/JJwn7NievskXcgubP/builds/m9YjZZ3FAfGvGEUo0/openapi.json