What is taxonomy classification, why does it matter, and how can it help you to stay ahead of the competition?
There’s a lot to address, so let’s take a closer look.
What is Taxonomy Classification?
It is estimated that over 250,000 new websites are created every day, adding to the billions that already exist and the hundreds of millions that publish content on a regular basis. That’s a lot of data and a lot of information, and as with everything else on the web, only a fraction of it is useful and relevant.
You can’t be expected to sort through this data manually. Even if you had the time and the patience, new data is created faster than you can analyse and record it—you just can’t keep up.
That’s where taxonomy classification comes in, also known as topic classification and topic modelling.
Taxonomy classification services, such as those provided by Identrics, help to automate this immensely time-consuming process. They can help you to learn more about an organisation, entity, or brand, and provide some much-needed clarity.
How Does Taxonomy Classification Work?
Taxonomy classification uses machine learning to trawl through hundreds of thousands of documents and find relevant and useful information. It can then provide a readable hierarchy of data, with all vital info broken down into easily digestible segments.
As an example, let’s suppose that your goal is to glean information about a specific product or service as it relates to a certain brand. Using Identrics taxonomy classification, you can find relevant and unbiased information relating to this product. This information is taken from a variety of online data sources and as it’s an automated process, your input is minimal and our machine learning software will do all of the work for you.
The information will then tell you exactly what you need to know, highlighting everything from opinions and complaints to potential errors and oversights.
What Do I Need?
Taxonomy classification can’t simply trawl the entire internet and then categorise everything that relates to your brand, products, or services. You need to define specific criteria for it to work.
In the above example, for instance, you may choose to highlight “functionality”, “reliability”, and “useability”, all of which can tell you more about how your product is being used.
A trained algorithm will have an idea of what to look for based on these criteria. Once you have that information, you can tell it to search through a specific dataset, such as customer support emails, user reviews, or support tickets.
If it returns a wealth of data telling you that there is a specific issue with an aspect of the product, you can fix the issue and fine-tune the algorithm to look for other data points.
How We Do It
Taxonomy classification is a complicated process, but we have developed an automated process that is fast and simple. It’s easy to use and can search seemingly endless data to find the results that you need.
Here’s how it works:
We Predefine Topic Taxonomy: The first step is a form of data clarification. We predefine what it is that needs to be found and feed this into our machine learning algorithm. As a result, we’re able to scour and classify hundreds of thousands of documents.
Data From Many Sources: Our taxonomy classification API gathers information from numerous sources. This expands the data pool and allows us to provide more of a holistic approach and a complete understanding of the topic at hand.
Rapid Results: In very little time, you will get the results that you seek in a clear and comprehensive manner.
What is Machine Learning?
Machine learning is integral to the data classification process. In simple terms, machine learning is a type of artificial intelligence (AI) that works by learning from human data.
The algorithm is fed text, tags, and other predefined data so that it can learn what to look for. This “training data” lets the AI fine-tune its methods and find relevant patterns.
Topic Modelling vs Topic Classification
Both topic modelling and topic classification can be utilised in similar ways, but there are key differences between these two methods.
Topic modelling is also known as “unsupervised machine learning”, as it doesn’t require a predefined set of criteria. It also doesn’t need any kind of training on its use, so it’s more accessible. However, you can’t guarantee the same accuracy that you get from topic classification, which uses predefined lists to ensure that only the most relevant and accurate results are produced.
Both topic modelling and topic classification can be effective ways to categorise large amounts of data.
Why is Taxonomy Classification Important?
The importance of taxonomy classification services largely depends on your brand and your place in the market, but every company that relies on data can benefit from these services.
The main goal is to stay ahead of the competition, and there are several ways that it can help with this.
Firstly, taxonomy classification methods can keep your content evergreen and ensure you’re producing content that people actually want to read. If you have good content, you will drive more organic traffic to your brand.
Taxonomy classification categorisation helps to highlight promotable, shareable, and reusable content, all of which play a role in developing an effective content marketing strategy.
As noted above, taxonomy classification can also help you to highlight issues with products and services. It can give you better insights into your brand and how your customers interact with it, highlighting concerns and problems that you may have otherwise missed.
Summary: Taxonomy Classification
The world is moving at a rapid pace. If you want to keep up, you need to move even faster. Machine learning is one of the ways that you can do this. At Identrics, we have the tools you need to get on top and stay there.
We can help you to stay one step ahead of the competition, and our taxonomy classification services play a big role in this. Find out more about our Taxonomy Classification service here