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Join Apify Instagram Comments Scraper with Bright Data LinkedIn
Top companies trust Datastreamer to integrate, enrich, join, and apply their web data needs.
About Apify Instagram Comments Scraper
Retrieve comments from posts by calling Apify's Instagram Comments Scraper within your pipeline!
To set up this Actor, you will need to add Instagram posts or reels to extract the comments from, the desired number of comments, and optionally, the order of comments, and replies.
For each Instagram post, you will extract:
Comment details: comment text, timestamp, and number of likes.
Commenter profile: username, full name, profile picture URL, and account status (private or public).
Engagement data: number of replies and whether the commenter is verified.
Post association: URL of the Instagram post the comment belongs to.
Replies (if any): nested replies under the main comment.
Collect and extract LinkedIn company profiles and people profiles using URLs. Returns matching company that include the location, industry, number of employee; and people profile that include the location of the user, current company, experiences, number of connections and more.
Quickly connect Apify Instagram Comments Scraper and Bright Data LinkedIn with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Apify Instagram Comments Scraper
For robust enterprise data integration, web data acts as a foundational source. It can be drawn from a variety of channels—including third-party partners, internal applications, and public web repositories.
Step 2
Add Bright Data LinkedIn with Unify or another transformer to combine schemas
To accelerate using your web data, you can apply any number of operations to the data. Enrich, augment, join, structure, filter, storage, search, or more! Datastreamer has hundreds of plug-and-play operations that you can apply.
Step 3
That's it! You have just connected Apify Instagram Comments Scraper and Bright Data LinkedIn
With Datastreamer it’s never been easier to use web data. You can dynamically expand your Pipelines with more capabilities, and you’ve now been able to solve your operational bottlenecks in working with web data.