Polish inflects everything, including names. Keyword matching that works in English finds a third of the mentions here and reports the rest as absent.
ScrapeIt runs the collection as a managed service. You name the sections, topics or entities; we build the pipeline with Polish lemmatisation for entity matching, an access flag on every row, regional edition as a field, and hand back CSV, JSON, Excel or a push into your warehouse.
Where a brief spans several Polish outlets we run them on one schema with editorial positions recorded, so an aggregate is interpretable rather than an accident of which sources were available.
We collect what is served publicly and do not circumvent the subscription. Crawl rules are honoured and requests paced. Content is copyrighted, so the dataset is for measurement and analysis rather than republication, and your counsel should see the use case before the project starts.
Article records carry the headline, standfirst where published, publication timestamp, section, author where bylined, the address and an access flag recording whether the full text was served or sits behind the subscription.
The access flag matters more than it sounds. A corpus mixing full articles and headline-only records without distinguishing them produces word counts and sentiment scores that are meaningless, because half the rows are three sentences and half are two thousand words.
Entity extraction is built for Polish rather than adapted from English. Polish inflects nouns and proper names through seven cases, so a politician's surname appears in several different written forms, and a matcher looking for the nominative finds a fraction of the mentions.
Section and regional edition are kept as fields, since this outlet publishes regional content alongside national and the distinction matters to anyone measuring local coverage.
Every row carries the collection timestamp and, where re-collection shows a change, a revision count.
Coverage measurement is the core deliverable and works well on metadata alone: how often a topic, company or person appears, in which sections, with what timing. It needs the lemmatised matching to be trustworthy and it does not need full text.
Regional edition analysis is a distinctive use here. National coverage and regional coverage answer different questions, and an outlet publishing both lets a client see where a story travelled beyond the capital.
Timing analysis - when a story breaks in this outlet relative to others - is straightforward once timestamps are collected consistently, and it is one of the better uses of a multi-outlet Polish corpus.
Limits stated plainly: full article text is behind a subscription and we do not circumvent it. Content is copyrighted, so even the metadata dataset is for measurement rather than republication. And one outlet is one voice - a Polish media picture needs several, with their editorial positions recorded.
Gazeta Wyborcza is one of Poland's largest and most influential newspapers, covering national politics, business, culture and regional news, with a long-running and clearly stated editorial position in Polish public life.
It runs a subscription business, and that sets the boundary of any collection here. An unauthenticated request is served the front page, section listings and article metadata; full article text sits behind the subscription. We collect what is served and do not circumvent anything, and we say which side of that line a brief falls on before quoting.
Within that boundary the material is substantial. The front page alone carries a great deal of structured content - headlines, standfirsts, sections, timestamps - which is what most media monitoring actually runs on.
Section pages respond directly. Crawl rules are published and disallow the search endpoint, servlet paths and advertising tooling, leaving editorial listings open.
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Media monitoring in Polish fails in a specific and predictable way, and it fails silently.
Polish is heavily inflected. A surname changes form depending on its grammatical role in the sentence, and so do the names of parties, institutions and cities. A keyword matcher built for English looks for one string and finds the subset of mentions that happen to use it, then reports the rest as absent. The output looks like data and understates coverage by a large and unknowable margin.
Doing it properly means lemmatising Polish text and matching on the base form, which is well-established work but has to be built in rather than bolted on. We build it in, and where a client is comparing Polish coverage against another language we say plainly that the two pipelines are not equivalent unless both do this.
The second reason is the subscription boundary. A dataset here is mostly headlines and metadata, which is entirely sufficient for coverage measurement, topic tracking and timing analysis, and insufficient for anything needing full text. Knowing which of those a client needs is a scoping conversation, not a delivery surprise.
The third is editorial context. This outlet has a declared political position, which is a fact about the source rather than a criticism of it, and a Polish media dataset that does not record outlet stance produces aggregate sentiment that is really a measure of which outlets were sampled.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, maintains the Polish entity matching as new names enter the news, and repairs the collector before a coverage series develops a gap.
You see a sample first, in your format, over the topics you actually track, with lemmatised matching applied so you can check on real headlines how many mentions a naive matcher would have missed.
Only where it is served publicly, and we record which rows those are. Full text sits behind the subscription and we do not circumvent it. For coverage measurement, topic tracking and timing, the metadata is sufficient - we say which your brief needs before quoting.
Because Polish inflects proper names through seven cases, so a surname appears in several written forms. A matcher looking for one string finds a fraction of the mentions and reports the rest as absent - output that looks like data and understates coverage by an unknowable margin.
Because a corpus mixing full articles and headline-only records without distinguishing them produces meaningless word counts and sentiment scores - half the rows are three sentences, half are two thousand words, and nothing downstream can tell which.
Yes, as a fact about the source rather than a judgement. A Polish media dataset that omits outlet stance produces aggregate sentiment that is really a measure of which outlets happened to be sampled.
Yes - regional edition is its own field. This outlet publishes regional alongside national content, and seeing where a story travelled beyond the capital is one of the more distinctive things it supports.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
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