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When working with the semantic core for commercial projects, the question often arises of how to massively filter information keywords that are not suitable for a given SL. If services such as OverLead can be used to automatically clean up search queries, it is difficult to do without human intervention to check new semantics.
In this article, I'll share how I approach this process, and at the end I'll provide a link to my search blacklists and share best practices for using them.
- Minus words
- Parameters (completeness, frequency, geo, etc.)
I will not disclose my criteria by parameters, but I am ready to share lists of negative keywords, since these phrases are available to everyone and are, in fact, a collective product.
This list mainly contains information requests that are generally not relevant for typical commercial projects. Before using this list, it is important to study it carefully, as thoughtless use can be harmful. For example, the list contains queries such as “install”, which may be useful in the context of software, but for a business related to the installation of plastic windows, such phrases would be inappropriate.
This list was compiled according to data from Wikipedia (at the time of its creation - cities with a population of more than 100 thousand). When collecting semantics for business, for example, in Yaroslavl, queries often contain keywords mentioning large cities, such as Moscow. In order not to add such keys to the emergency situation, I use this list and exclude unnecessary cities.
The principle of working with this list is similar to the previous one. If you are collecting semantics for Ukraine or Belarus, be sure to exclude those cities that do not suit you.
Lists of female and male names are useful if people's names appear frequently in queries, especially in cases where the brand has a name. This may be relevant, for example, when working in the service industry for photographers.
All of the above and other lists of negative keywords can be downloaded through my Yandex.Disk, the link to which is provided below.
In Yandex.Direct, you can add no more than 2048 characters to negative keywords. However, my lists are only 3 - 5682 characters long, which is over the limit. To solve this problem, I use the following method: I put the entire list into the collector, select my region and parse the frequency according to the base frequency. Thus, you can cut off unimportant requests and reduce the list to the desired limit.
To create a list of negative keywords for a specific advertising campaign, you can use the following technique:
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