Introduction
Machine learning has revolutionized the way content marketers create content. It provided detailed information about what search engine bots actually crawl and how they understand natural language.. Writing content today is very different from what it was ago. 15 years. In the past, Content was created for search engines, which was enough to rank the website in a prominent place. But, today, Valuable content is not content created specifically for search engines.
In fact, The creation of such content may even have an opposite effect and cause your website to be evaluated as unauthorized and, in the worst case, Penalized. No, Today's valuable content should focus the target reader and answer their questions.. You need to nurture the target reader by providing them with all the essentials in written form so that they can solve the problem they searched for on Google.. Therefore, Machine learning and natural language processors exist today to facilitate this process.. Here's everything you need to know about the basics of natural language processing and its practical application.
What is natural language processing?
Natural language processing, or soon NLP, is the automatic manipulation of natural language (Voice and text) Performed by Software. With the development of computational linguistics, that uses computer tools to study linguistics, NLP became an inevitable part of how search engines understand what the content you've written is about..
The main objective of NLP is to characterize and explain the multitude of linguistic observations that surround us.. Related to this is the cognitive side of language., that it is about understanding and receiving language. Machines are trying to understand speech and text the same way we humans do.. Therefore, NLP covers any type of computer manipulation of natural language. Includes a wide range of activities, from counting word frequency to comparing different writing styles.
With the pace of development, NLP is trying to achieve its ultimate goal: understand complete human expressions and be able to give them meaningful answers. To do it, It involves the automatic computational processing of human languages that have algorithms for understanding human-produced text as input and algorithms that produce natural-looking text as outputs..
Google Cloud NLP API
With Google's own NLP API, Understanding how search engine bots evaluate text quality is closer to each content analyst. The tool uses machine learning to reveal the structure and meaning of text. You can see what information about people, places and events Google extracts and considers relevant, so you can use this information to create content that Google will easily understand and bring you closer to the target reader..
Using Google's NLP API, You can take a look at the syntax analysis of a piece of content, Extract tokens and sentences, Identify parts of speech and create dependency analysis trees for each sentence. What's more, There is entity analysis that identifies entities within content and labels them by type: date, person, contact information, organization, Location, events, Products & Media.
Later, Sentiment analysis shows you the opinion, general feeling or attitude expressed in a block of text with a sentiment score. Finally, Customs classification shows how it classifies content into more than 700 predefined categories.
Who benefits most from using the NLP API?
As can be concluded from the above, NLP is used to understand the structure and meaning of human language, which is then transformed into rule-based machine learning algorithms that solve specific problems and perform desired tasks. Content marketers, Strategists and writers can use it to understand how computers understand human language and use this knowledge when crafting their own writings..
These are the keyword phrases that are used within the content: direct NLP API tool to categorize content appropriately. But nevertheless, It's not that simple. All semantic features, morphological and syntactic are included in the processing. The general benefits of the NLP API are as follows:
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Extensive analysis
With the tool, Machines can automatically understand and analyze a large amount of unstructured text data, from comments on social networks, reviews, news, Articles and many more.
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Classify information in real time
The tool helps the machine to classify all the information about the content in real time without the help of humans. And best of all, it's done quickly., Accurate and efficient.
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Tailored to the niche
The fact that you can use the NLP API tool to understand how you evaluate content specific to your needs and criteria is what makes it a cutting-edge tool.. Keep in mind that you are developing so much that you can understand complex, industry-specific language, including sarcasm and misused words.
How does the NLP API work??
It's a complex process, but to explain it simply, It should be known that the tool transforms the text into a machine-understandable unit by vectorizing the text. Later, Training data and expected results are sent to machine learning algorithms so that associations can be made between inputs and outputs. From now on, Machines use statistical analysis methods to build their own “Knowledge Bank”. To fully understand this, You should also know what text classification is and how it provides the necessary data..
Practical examples of analysis
Let's take a look at how to parse a piece of text with the tool.
Let's use this as an excerpt:
"Since the advent of globalization, The environment in which a company operates is constantly changing. An important component of the business environment is the technological environment. The technology, too, as we all know, is constantly changing, updating with the new trends that arrive every day.
Therefore, It is imperative that companies understand and keep up with technology trends to survive in the market..
One thing that has taken the IT industry by storm is machine learning and artificial intelligence.. AI and ML have countless applications that can update and transform the way your business works..
Then, whether or not your company is in IT, it is essential that business leaders are aware of AI and ML trends and can keep up with the pace and changes of the business environment “.
From the article Quick Guide to the AI and Machine Learning Universe for Business Leaders on the DataPeaker Blog.
Entity analysis
Sentiment analysis
Syntax analysis
Categorization
As you can see in the images above, The content is discussed in 4 categories: entities, feeling, Syntax and categories. The analysis provides insights into how machine learning processes perceive written content and how it is fed to search engine bots. All the data that is analyzed with the tool is actually crawled by the search engine crawlers and receive the information about what it is and for whom it is intended.. What's more, Crawlers can determine the reliability and authenticity of content from analysis.
The opportunity for anyone to explore how the NLP API sees and understands content provides feedback to these people who want to create optimized content ready to deliver valuable insights to target audiences.. It's a cycle where content marketers can engage and leverage insights into how machine learning works..
Comparison of two pieces written from the same niche
Now, Let's take a more investigative step: Let's look at two examples of text written on two different websites, how they are analyzed with the NLP API and which of them works best in the SERP.
"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.. ML is one of the most exciting technologies one would have come across.. As it follows from the name, gives the computer that makes it more similar to humans: The ability to learn. Machine learning is currently being actively used, perhaps in many more places than you would expect “.
"Machine learning algorithms are responsible for the vast majority of AI advances and applications you hear about.. (For more background, See our first flowchart on “What is AI?”)
What is the definition of machine learning?
Machine learning algorithms use statistics to find patterns in massive quantities * of data. And the data, here, They cover many things: numbers, words, images, Clicks, Whatever. Whether it can be stored digitally, can be fed into a machine learning algorithm.
Machine learning is the process that powers many of the services we use today: recommender systems like Netflix's, YouTube and Spotify; search engines like Google and Baidu; social media feeds such as Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.”
And here's how both outcomes work in SERP.
As you can see, the result contained in the Extract 1 is classified better than the result with the Extract 2 on the first page of Google. Notice how the NLP API has parsed the content and how the Extract 1 You get a higher score in magnitude and how, although both extracts are for “machine learning”, are classified differently: the first in Business & Industrial with a score of 0.56 while the second one is Science / Computer Science with a score of 0.83. The difference in categorization directs the content analyst to see what writing is appropriate for business and science. Taking this into account, The content writer will know what type of content to produce.
Key findings
What to consider and pay attention to when using the NLP API?
You should know that the NLP API has a demo version that you can try. But nevertheless, Its implementation and use is not free. Regardless, The benefits are huge. It greatly facilitates the way content marketers understand how machines understand human language and use this knowledge to create content for both the target audience and search engine bots.. It is strongly recommended to take advantage of its analytical features and tools.
Branko Ilishev
Co-Founder and Head of SEO at Smart Click, SEO and Web Agency offering digital solutions to help business website owners outperform their competitors and gain more market share with a high-converting website and powerful SEO strategies.
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