Scraper for search suggestions by keywords on Pinterest. The Pinterest suggestions scraper solves one of the main SEO tasks, namely the fast automated gathering of an extended semantic core. With the SE::Pinterest::Suggest scraper, you can automatically collect keyword databases from Pinterest search engine suggestions by query. Using the SE::Pinterest::Suggest scraper, you can easily and quickly scrape Pinterest suggestions by query.
Thanks to the multithreading capabilities of A-Parser, the processing speed can reach 6000 queries per minute, which on average allows obtaining up to 16000 results per minute.
You can use automatic query multiplication, substitution of subqueries from files, iteration of alphanumeric combinations and lists to get the maximum possible number of results. Using result filtering, you can immediately clean up the result by removing all unnecessary junk (using negative keywords).
A-Parser functionality allows you to save the scraping settings of the SE::Pinterest::Suggest scraper for further use (presets), set a scraping schedule, and much more.
Saving results is possible in the form and structure you need, thanks to the built-in powerful templating engine Template Toolkit, which allows you to apply additional logic to the results and output data in various formats, including JSON, SQL, and CSV.
- Query suggestions
- Type of suggestions
- Deep parsing using the Parse to Level feature
- Selection of the type of suggestions that will be used for substitution when parsing in depth
- Gathering keyword databases
As queries, you need to specify search phrases, for example:
Speak in english
Cats and dogs
You can use built-in macros for automatic substitution of subqueries from files, for example, we want to add some list of other words to each query, let's specify several main queries:
In the query format, we will specify a macro for substitution of additional words from the file Keywords.txt, this method allows to increase the variability of queries many times over:
This macro will create as many additional queries as there are in the file for each original search query, which in total will give
[number of original queries(domains)] x [number of queries in the Keywords file] = [total number of queries] as a result of the macro's work.
For example, if the file Keywords.txt contains:
As a result, the substitution macro will turn 3 main queries into 6:
Output Results Examples
A-Parser supports flexible result formatting thanks to the built-in templating engine Template Toolkit, which allows it to output results in any form, as well as in structured formats, such as CSV or JSON
Exporting the List of Suggestions
Output in a CSV Table
Saving in SQL format
[% FOREACH results;
"INSERT INTO serp VALUES('" _ query _ "', '"; suggest _ "')\n";
Example of result:
INSERT INTO serp VALUES('write essay', 'write essay for me')
INSERT INTO serp VALUES('write essay', 'write essay online')
INSERT INTO serp VALUES('write essay', 'write essay for you')
INSERT INTO serp VALUES('write essay', 'write essay free')
INSERT INTO serp VALUES('write essay', 'write essays')
INSERT INTO serp VALUES('write essay', 'write essay conclusion')
INSERT INTO serp VALUES('write essay', 'write essay on covid 19')
INSERT INTO serp VALUES('write essay', 'write essay today')
INSERT INTO serp VALUES('write essay', 'write essays for money')
INSERT INTO serp VALUES('write essay', 'write essay online for free')
Dumping results to JSON
A-Parser allows you to process results directly during scraping, in this section we have provided the most popular cases for the scraper SE::Pinterest::Suggest
Parse to level option
Filtering results (using negative keywords)
|Selection of the type of suggestions that will be used for substitution during deep scraping