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Natural Language in Search Engine Optimization (SEO) — How, What, When, And Why

Last updated: 05-04-2021

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Natural Language in Search Engine Optimization (SEO) — How, What, When, And Why

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Natural Language Processing
Natural Language Processing (NLP) refers to when machines express inferences by understanding human language and deriving the meanings it derives from it. As in machine learning, computers are taught at a level that can understand human language [2].
Natural language processing (NLP), one of the latest innovations and sub-branches of artificial intelligence, is an area that is continuously on-edge and bringing state-of-the-art research and development. To dive in further, NLP is a sub-branch of computational science and artificial intelligence that handles language data between machines, humans, and data sources such as text and sound [3]. Computational Linguistics contains all the grammar rules in the language, and the language is formalized and expressed with mathematical models.
The tutorial below gives us a straightforward outlook of natural language processing:
In addition to the tutorial above, check out the ‘Zero to Hero for Natural Language Processing’ video by TensorFlow.
How does natural language directly affect SEO?
According to research in recent years, Google’s search engine is much more powerful and successful in understanding websites than other search engines. And it is shown that machine learning, big data, and data analytics now enable page analysis without setting the context of keywords as SEO used to work back in the day [4].
For the working logic of SEO, it is critical to go to the top pages with semantic keywords, even on sample blog pages. In addition, search engine optimization, also known as SEO, is presented to the users by increasing the quality of the website with sample SEO tags.
Back in the day, search engines operated more mathematically without truly understanding what the user wanted. This flaw has given an obvious advantage to natural language processing. Because Google and other search engines were only looking at SEO tags when choosing which website to feature.
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Natural language processing acts according to its own syntax rules when analyzing a text or a sentence if it will be more specific [5]. When analyzing a sentence, check the meaning relation connecting the words before and after the “keyword” with each other — translation tools such as Google Translate or applications that check grammar with this logic [6].
An example NLP image
We show two sample words in the image above. Correction is provided by looking at the rule of the relationship between these two words. Just as natural language processing works when parsing websites. Assuming that there is no multi-language support when natural language processing does not work simultaneously with SEO, this is a problem [7].
However, the different languages used on the website are analyzed thanks to natural language processing, and it helps Google understand the content with natural language processing. Natural language processing enables text processing by controlling the previous and next concepts of the content on a website or a page. The SEO and UX (User Experience) solutions used to date have been supported with natural language processing. NLP is achieving the difficulty now.
There are certain strategies written for you to rank effectively in SEO solutions. By checking out this blog post , results can be produced with these strategies.
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????‍☠️ Spamdexing AKA Black-hat SEO
Spamdexing (search engine poisoning) is the deliberate manipulation of search engines [8]. Black-hat SEO or Spamdexing means fraudulently increasing the ranking of a website or a page in the search engine in some negative ways. However, Google and other search engines have successfully tackled this problem, without any violation of ethics, by introducing NLP to its practices.
Considering the operational structure of SEO and NLP, it has become the plan with the algorithm updates that allow the usage statistics in search engines to increase. BERT (Bidirectional Encoder Representations from Transformers) is a language model that enables Google to make a more precise analysis as a search engine [9].
Bidirectional Encoder Representations from Transformers (BERT) is a Transformer -based machine learning technique for natural language processing (NLP) pre-training developed by Google  [9].
Thus, higher result analyzes are performed by combining more colloquial and keywords with search terms.
SEO carries out a semantic search to make sense of the user’s queries through search engines. Suppose a user is searching. A search engine optimization has semantic meaning and mathematics at its backbone. The image below is an excellent example of this situation.
A user searches the search engine for the concept of “network.” Many different meanings can be made for the network search. And often, printouts are made for computer networks or clothing stores. This process depends entirely on your search space.
A figure from NLP & SEO to work together.
However, if you pay attention to the arrows in the figure above, the user may have searched for the term “neural” before. This process is where NLP and SEO come into play. NLP can give “neural network” outputs to the user by looking at the possibility of these two words working together. Therefore, search engine results will now provide outputs from neural networks.
Branching of sentence structures that follow each other [10]
R — reading the word, N — translating the character into lowercase, D(a) — removing the apostrophe, D(s) — removing the part of the word from the vowel-consonant, D(z) — deleting the ending, D(g) — deleting the vowel, D(p) — removing one consonant, D(m) — deleting the soft character. Here, the sequences are replaced by the Porter Stemmer algorithm and the word stop words are removed, and the desired NLP text is obtained.
Axioms for Search Analysis
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To understand the search engine optimizations, it is necessary to be closely recognized statistical and mathematical-based concepts.
Notation: SEO should use a standardized notation applicable to mathematics and statistics. This causes a search analysis to create a distinctive notation.
Search listing: The search engine and NLP are mentioned to be inferred on the texts. However, the search listing should use a search space to keep these keywords extracted. Any URL in search results is used in a document or website.
Query Result: A search result is the search results searching for the information results extracted as a particular limited. For instance, when the “Towards AI” concept is used for search, 591.000.000 results appear.
A sample of a search query in Google’s search engine.
But appear on a limited number of pages. For example, if there is a limitation in the range of 0 to 1000, it appears 1000 lists. This number is randomly determined by search engine optimization.
Query Space: The query space is a community where the word groups resembled together are an attempt to find. For example, with “sample graphic” and “sample of graphic,” is the cluster to be searched for relation to each other. Thus, more than one option will be dominated by more space, not a single word group!
Naturality: Assume that a web page is optimized for the keyword 1 contained. If this web page appears in search results for keyword 2, this web page will be considered a natural list for keyword 2. So here is Naturality dominated.
A search listing is natural if and only if it has not been optimized for the specific query result in which it appears [11].
Transparency: The word transparency comes from the opening. In a web page, it should discriminate the observer (optimized and not to be optimized) for the search engine results if optimized is transparent.
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Opacity: A query space is considered opaque on a page that is not explicitly optimized for searching ordinary observers in search results. Opacity or Search Opacity means that lists optimized for search results are not explicitly optimized for the query area.
A list is opaque if a corresponding web page or site is not related to the query, but if links are used to include the document in the search result.
What is the future of SEO?
SEO-friendly functioning of websites in the future will affect their ranking status. For example, while marketers take care in promoting their brands, they generally take care to work in harmony with SEO. The more responsive websites and pages look, the higher the results will rank. However, there are situations and times when this is not enough. Because, according to the rumor, everyone now pays attention to work in harmony with SEO [12]. This summarizes the situation that the ranking will not change much in the future.
Nowadays, when social media and brand awareness are high, the statistics about people visiting websites and using social media activities are quite high. A website should contain good content and quality brand information. If it doesn’t have these values, there will be no brand value [13]. In the future, marketers will have to understand their target audience. It is on the agenda that UX (User Experience) will beat SEO by far between plans! As search results become more complex, user experience (UX) will play a bigger role in search [14]. Thus, UX will manage the future by working towards more users.
How do Semantics and Mathematics Directly affect SEO?
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Semantic search is the way users act on a Search engine according to the semantic-meaning-relationship of words and concepts [15].
Search engines place frequently searched queries on a concept that exists on websites in a semantic structure. This semantic query contains some bases that will also work well for UX. Hence it deals with user behavior. Semantic SEO manages the concepts found in search engines by pulling them into the query network.
Example of knowledge graph
Google has prepared new technologies for Semantic Search and Semantic Web. In fact, this situation is common in daily life. The background of this work is basically based on graph theory. The graph you see above actually holds contextual information in terms of content.
Each node contains information contained in web content. Each colored node in the image above is considered a different search space. There is a data interaction between these graphs. In this way, by expanding the general search space, more results and higher ranking in SEO are dominated. Mathematical infographics are used to control the content of a website or a page. Google has also made technological advances in asset recognition with the infographic they call knowledge graph. A graph is made up of nodes and the edges that connect these nodes. And a graph is a structure that determines a set of objects to which pairs of objects are “related” in a sense.
Google Knowledge Graph has been launched in May 2012 with the motto of “Things, not strings”. In the beginning, Google Knowledge Graph has included 570 million entities and 6 billion facts about these entity connections. In 2020, Google has 500 million facts and 70 billion entities in the Knowledge Graph [15].
Knowledge panel data about Thomas Jefferson displayed on Google Search , as of January 2015 [16]
Users searching for the vice president in the panel are queried from the web browser, and the other presidents they have searched before are kept in the users’ history. The other presidents for UX support are also shown. This result indicates an essential background in semantics and mathematics, where search engines take action by retrieving the content in knowledge graphs.
Final thoughts
As we saw, search engine optimization is continuously transforming, and with the use of natural language processing, SEO is focusing more and more toward a search experience optimization to drive only the best and high-quality results to those using search engines, and a company excelling at these tactics and innovations, is Google.
If your business or startup needs help with your SEO , please feel free to contact us , and we’ll be happy to assist you. Thank you for reading!
[1] “Definition of NATURAL LANGUAGE.” www.merriam-webster.com, 23 Apr. 2021, https://www.merriam-webster.com/dictionary/natural%20language . Accessed 28 Apr. 2021.
[2] “Natural Language Processing (NLP) With Python — Tutorial”. 2021. Medium. https://pub.towardsai.net/natural-language-processing-nlp-with-python-tutorial-for-beginners-1f54e610a1a0.
[3] ”Natural Language Processing (NLP) and How It Affects SEO.” www.webfx.com, 16 May. 2019, https://www.webfx.com/blog/seo/what-is-natural-language-processing/. Accessed 28 Apr. 2021.
[4] Davis, Rudi. “How Natural Language Processing SEO Will Affect Businesses, Media | Publicize — PR Firm.” publicize.co, 22 Sept. 2020, https://publicize.co/community/natural-language-processing-seo-will-affect-businesses-media/. Accessed 28 Apr. 2021.
[5] Ismail, Kaya. “How Natural Language Processing Can Help With SEO.” www.cmswire.com, 13 Jan. 2020, https://www.cmswire.com/digital-marketing/how-natural-language-processing-can-help-with-seo/. Accessed 28 Apr. 2021.
[6] Wikipedia Contributors. “Natural Language Processing — Wikipedia.” en.wikipedia.org, 26 Apr. 2021, https://en.wikipedia.org/wiki/Natural_language_processing. Accessed 28 Apr. 2021.
[7] Wikipedia Contributors. “Search Engine Optimization — Wikipedia.” en.wikipedia.org, 28 Apr. 2021, https://en.wikipedia.org/wiki/Search_engine_optimization. Accessed 28 Apr. 2021.
[8] Wikipedia Contributors. “Spamdexing — Wikipedia.” en.wikipedia.org, 28 Apr. 2021, https://en.wikipedia.org/wiki/Spamdexing. Accessed 28 Apr. 2021.
[9] Wikipedia Contributors. “BERT (Language Model) — Wikipedia.” en.wikipedia.org, 18 Feb. 2021, https://en.wikipedia.org/wiki/BERT_(language_model). Accessed 28 Apr. 2021.
[10] Lytvyn, Vasyl. “Mathematical Model of Semantic Search and Search Optimization.”
[11] ”SEO Math: Axioms for Search Analysis.” www.seo-theory.com, 9 Dec. 2008, https://www.seo-theory.com/seo-math-axioms-for-search-analysis/. Accessed 28 Apr. 2021.
[12] Post, Huffington. “The Future of SEO: It’s Not What You’re Expecting.” neilpatel.com, 24 July. 2018, https://neilpatel.com/blog/the-future-of-seo/. Accessed 28 Apr. 2021.
[13] “The Future of SEO: What SEO & Marketing Pros Need to Understand.” www.searchenginejournal.com, 30 Oct. 2019, https://www.searchenginejournal.com/future-of-seo/326529/#thefutur.. Accessed 28 Apr. 2021.
[14] “3 Predictions About the Future of SEO.” searchengineland.com, 2 May. 2017, https://searchengineland.com/3-predictions-future-seo-273758. Accessed 28 Apr. 2021.
[15] Koray, Theoretical Seo. “What Is Semantic Search? How Does Semantic Search Affect SEO? — Holistic SEO.” www.holisticseo.digital, https://www.holisticseo.digital/theoretical-seo/semantic-search/#Why-is-Semantic-Search-Important-for-SEO. Accessed 28 Apr. 2021.
[16] Wikipedia Contributors. “Google Knowledge Graph — Wikipedia.” en.wikipedia.org, 1 Apr. 2021, https://en.wikipedia.org/wiki/Google_Knowledge_Graph. Accessed 28 Apr. 2021.
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