Blog comments API (BETA): Extract blog comment data at scale

A reliable and scalable way to tap into blog comment driven insights

We are excited to announce our newest Automatic Extraction API. The Blog Comments API is now publicly available as a BETA release.

If you want to skip the introductions and just get stuck in, here are the links you need:

What does the Comments API Beta release achieve?

Zyte Automatic Extraction Comments API sets out to bring the power of our automatic data extraction capabilities currently used for applications such as media monitoring, job postings, and more into the arena of blog comment analysis.

The underlying data model for the API was released to production as part of 20.6.0 release of AutoExtract.

Delve deeper into the world of sentiment analysis with blog comment analysis

Customer support management presents many challenges due to the sheer number of requests, varied topics, and diverse departments within a company that might have a say in resolving the matter.

Sourcing structured data from blog comments as provided by our API can be used in tandem with natural language understanding (NLU) solutions to quickly and effectively identify, track and act upon particular conversation strings ‘hidden’ amongst the noise of thousands of comments. You are effectively highlighting warning signs that your CX team should become involved before an incident takes place.

Another particular powerful insight that can be derived from comments revolving around the sphere of Voice of Customer (VoC) and product analysis. By tapping into blog comments, you can search keywords for a particular product or feature or use the parsed data to train sentiment analysis model to find only the information you need.

Get machine-readable, structured blog comment data without code!

Without our Comments API (Beta), you would need to write custom code for each blog post to extract and parse the data. Let alone the time and overhead spend required to maintain the necessary infrastructure to deliver the data in real-time, in a scalable fashion and reliably.

Our APIs allow you to focus on the data, not harvesting it!

We are continuously improving the underlying AI technology, so you can be assured you get the highest quality comment data possible.

How to use It?

Using the API is simple:

First, provide a feed of page URLs you want to extract data from into AutoExtract as an API Request.
Get a coffee (or whatever is your drink of choice), lay back and let the API do it’s magic!
Voilà! Structured, machine-readable comments data directly into your environment in JSON.

Here’s an example response:

[ { "comments": { "url": "https://example.com/article-with-comments", "comments": [ { "text": "A comment on article", "datePublished": "2020-01-30T00:00:00", "datePublishedRaw": "Jan 30, 2020", "upvoteCount": 12, "downvoteCount": 1, "probability": 0.95 }, { "text": "Another comment", "probability": 0.95 } ] }, "webPage": { "inLanguages": [ {"code": "en"}, {"code": "es"} ] }, "query": { "id": "1564747029122-9e02a1868d70b7a3", "domain": "example.com", "userQuery": { "pageType": "comments", "url": "https://example.com/article-with-comments" } ]

[ { "comments": { "url": "https://example.com/article-with-comments", "comments": [ { "text": "A comment on article", "datePublished": "2020-01-30T00:00:00", "datePublishedRaw": "Jan 30, 2020", "upvoteCount": 12, "downvoteCount": 1, "probability": 0.95 }, { "text": "Another comment", "probability": 0.95 } ] }, "webPage": { "inLanguages": [ {"code": "en"}, {"code": "es"} ] }, "query": { "id": "1564747029122-9e02a1868d70b7a3", "domain": "example.com", "userQuery": { "pageType": "comments", "url": "https://example.com/article-with-comments" } ]

Learn more about the API in the documentation.