> For the complete documentation index, see [llms.txt](https://help.listly.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.listly.io/support/ecommerce-review-scraping/ecommerce-websites/tripadvisor-scraper.md).

# Tripadvisor

<figure><img src="/files/rBTp892YVzuvB5gUMHLg" alt=""><figcaption></figcaption></figure>

{% hint style="success" %}
**Ready to scrape?** No login required! Preview the sample data, explore the available fields, and download a free sample set.
{% endhint %}

{% tabs %}
{% tab title="Sample Data" %}
{% file src="/files/dNFjAZw9afrn1vFk8s88" %}

Test page: <https://www.tripadvisor.com/Restaurant_Review-g32655-d3701882-Reviews-N_naka-Los_Angeles_California.html>
{% endtab %}

{% tab title="Available Fields" %}

| Field                                                             | Description                                                         |
| ----------------------------------------------------------------- | ------------------------------------------------------------------- |
| `review-title`                                                    | Headline of the customer review                                     |
| `review-body`                                                     | Full text of the customer review                                    |
| `rating`                                                          | Overall bubble rating (1–5)                                         |
| `food-rating / service-rating / value-rating / atmosphere-rating` | Per-category bubble sub-ratings (1–5), when the reviewer gave them  |
| `visit-date`                                                      | Month/year of the visit                                             |
| `trip-type`                                                       | Trip types such as Couples, Family, Friends, Solo, Business         |
| `review-date-written`                                             | Date the review was published                                       |
| `collected-in-partnership`                                        | Whether the review was solicited in partnership with the restaurant |
| `reviewer-name`                                                   | Display name of the reviewer                                        |
| `reviewer-location`                                               | Reviewer's home location                                            |
| `reviewer-contributions`                                          | Total reviews the reviewer has contributed                          |
| `photo-count`                                                     | Number of photos attached to the review                             |
| `review-photo-urls`                                               | Links to the attached review photos                                 |
| `reviewer-profile-url`                                            | Links to the reviewer's TripAdvisor profile                         |
| `reviewer-avatar-url`                                             | Links to the reviewer's profile picture                             |
| {% endtab %}                                                      |                                                                     |

{% tab title="Use Cases" %}
**Category-Level Performance.** Break reviews down by the food, service, value, and atmosphere sub-ratings to see exactly where the restaurant excels and where it falls short — far more actionable than a single overall score, and ideal for pinpointing what to fix or promote.

**Trip-Context Segmentation.** Segment sentiment by trip type — Couples, Family, Friends, Solo, Business — and visit date to learn how the experience lands for different guests and how it shifts across seasons, invaluable for tailoring offerings and messaging.

**Solicited vs. Organic Review Analysis.** Separate reviews collected in partnership with the restaurant from spontaneous ones to gauge how incentivized feedback skews the ratings, then weight your analysis and testimonials toward credible, organic voices.

**Visual Social Proof & Reviewer Authority.** Surface photo-rich reviews from high-contribution reviewers to feature the most credible, image-backed testimonials — real guests, real plates, shown as served.
{% endtab %}
{% endtabs %}

<p align="center"><a href="https://gist.github.com/listly-io/69b4902add3018515d77851036d84e18" class="button primary">Code</a></p>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://help.listly.io/support/ecommerce-review-scraping/ecommerce-websites/tripadvisor-scraper.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.