By Dan Duke – Understanding natural language processing and the way it informs machine learning can dramatically improve your content marketing efforts. Read on to learn all about this tool and how it can help drive a greater audience to your company.

What is Natural Language Processing?

Natural Language Processing (NLP) is a tool that uses computer science, AI concepts, linguistics, and data to allow computers to understand human language – both written and verbal. It converts written or spoken words into data and makes it possible for devices to communicate with each other.

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These connected devices have changed our world – think of the "smart home," for example – allowing us to use voice-based or text-based solutions (from our phones, typically) to complete actions from a distance, whether that's turning on the lights, adding to your grocery list, or setting the temperature on the hot tub. Verbal command capability and chatbots (query management) have joined the scene, along with climate control devices (thermostats) and home monitoring devices (security systems).

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We may not be aware that the response to our inquiry is generated by an algorithm, the driving force behind NLP, but it often is. NLP offers the ability to program computers to analyze vast amounts of data from natural language. It allows us to use data from chatbots, social media posts, documents, and website pages. This tool is finding its way into marketing as well, and that’s what we’re going to discuss.

The importance of natural language processing

NLP allows machines to understand human language through complex comprehension. It bridges the human-to-machine gap by using Artificial Intelligence (AI) to process text and the spoken word. It’s a form of AI that manipulates and interprets human language using computational linguistics (the parsing out of parts of speech and words into data).

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NLP consists of two branches. First is Natural Language Understanding (NLU). This extracts the meanings of words by studying the relationships between them. Then, Natural Language Generation (NLG) transforms data into understandable language, writing sentences and even paragraphs that are appropriate, well-constructed, and often personalized.

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NLP allows computers to create responses for chatbots and virtual assistants, write subject lines for emails, and even compose advertising copy and marketing tools. Here’s how to think about it: NLU focuses on your computer’s ability to read and listen (speech-to-text processing), and NLG allows it to write and speak (text-to-speech). Both are part of NLP.

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NLP is everywhere:

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• Intelligent Personal Assistants (IPAs) answer customer questions.
• Voice assistants like Siri respond to commands.
• Marketers use it to create custom content, personalize or push specific promotions, and personalize offerings to the taste of a particular shopper.
• Auto-complete and auto-correct functions in texting help you write and sometimes drive you nuts.
• Machine translation tools clue you into words from other languages.

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Even brick-and-mortar stores can take advantage by customizing individual stores’ landing pages to show local hours of operation, addresses, directions, and additional information.

How does NLP work?

How in the world can a machine understand the spoken or written word? Well, it’s all about linguistic analysis. Natural language processing allows a computer to translate spoken or written words into data, including all of our colloquialisms, regionalisms, misspellings, and abbreviations. It’s astonishing, really, that this can be done, given all of our personalizations of speech. This is where computational linguistics comes into play. Programs can break down any text into four parts: tokens, semantic information, syntactic information, and context information. The computer deals with each piece separately.

Tokenization

This is where it all starts. Segmentation is the first step of breaking down human language into units that a computer can understand. Tokens are typically words, subwords, or characters. Tokenization is a fundamental pre-processing step, enabling computers to understand and analyze human language.

Semantic information

This is the actual meaning of a specific word.  You can interpret a sentence like “He is enjoying the date” in different ways, depending on the meaning of “date.” Is the gentleman out on the town with a friend or chewing fruit from a palm tree? Knowing which form of the word “date” to use — knowing the relevant definition in this specific instance — is critical in understanding the meaning of that sentence.

Syntax information

This refers to the sentence structure. We’ve moved from the word level to the phrase level. “Sarah died peacefully with her family on September 4.” Oh my – how many people died?  The whole family, or just Sarah? That’s an important distinction! How can a computer figure that out?

Context information

The relationship of words, phrases, or sentences to each other is the key here. How should it be understood if someone says, “Man, that’s hot!” Is the item’s temperature high? Or is the speaker describing something fashionable or something to be desired? So how can a machine be taught to analyze these distinctions? It’s not hard to teach a system to understand the basic rules and patterns of a language — that’s the science of linguistic computation. It just takes a lot of time. And (as we saw in the “hot” example above), the rules can’t always keep up with the evolution and revisions of language.

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The earliest text-mining systems were based entirely on patterns and rules, but as NLP and machine learning evolved, hybrid machine learning grew. Hybrid machine learning uses those same rules and patterns in both supervised and unsupervised models, and there are several different versions, using low-, mid-, and high-level text functioning. At the low level are the first processes that consider any input text. They turn the unstructured text into data. At the middle level are the text analytics that extract the content (Who’s speaking? What’s being said? What is being talked about?). At the high level is the sentiment analysis.

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As we’ve seen, the meanings of words change with the speaker’s intent and the listener’s expectations.  Machine learning and NLP offer solid solutions for analyzing words, but each system must be tuned or trained to match the user’s needs.

Growing from GPT-3 to GPT-4

OpenAI, an AI research and deployment company, has made significant advancements in the field. Its GPT-1 and GPT-2, the first and second versions of the Generative Pre-Trained Transformer, were the original forays. GPT-3 represented a giant leap and is still available for free. It can:

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• Use internet data to generate text.
• Take a small bit of input text and generate a large amount of sophisticated machine-generated text.
• Analyze and input a piece of language and predict what the writer or speaker meant to infer.

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GPT-3 can be trained, for example, to compose tweets or press releases or even computer code. GPT-3 uses (NLG) to create easily understood responses. Chatbots often use this. Some businesses use it to develop copy for headlines, scripts, and summarizations. And some online content marketing services use it to generate keywords automatically. GPT-3 has brought deep learning, a form of machine learning, and other artificial intelligence tools into everyday use, fueling social media copywriting and content generation.

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GPT-4, available with a monthly subscription, has further broadened and strengthened the use of NLP. This version is not “just a language model.” It also can consider the visual world by including images as an additional input type. This means that Chat GPT-4 can generate text outputs based on combined text and image inputs.

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The implications of this shift are substantial. Chat GPT-4 can generate captions for images, classify visible elements within images, and even analyze the content of images.

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Another one of the most significant distinctions between GPT 4 is that it's both more creative and reliable. It also can respond accurately to more nuanced prompting, and its multilingual capabilities make it more versatile and inclusive.

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It's also able to process text on a much larger scale. Chat GPT-3 had a maximum context length of 4,096 tokens, while the Chat GPT-4-32K variant can handle 32,768 tokens.

Content marketing and NLP

Natural language processing can be a vital component of a content marketing plan, given its many uses. Highlights include:

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• Analyzing content for sentiment
• Helping to determine which keywords will be most relevant
• Writing product descriptions for digital commerce sites
• Helping to develop a marketing strategy by assessing the content of a particular client or by performing a content audit
• Refining chatbot functions to enable the gathering of solid leads.

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And it is a growing science. A Fortune Business Insights report estimates that the value of the global NLP market will grow from $29.71 billion in 2024 to $158.04 billion in 2032.

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‍The e-commerce company Alibaba’s digital marketing wing (Alimama) offers an AI-powered tool for copywriting that uses NLP from millions of language samples to generate copy. The tool is easy to use. An advertiser inserts a link to a product page and clicks the button (Produce Smart Copy) to receive content ideas. The data scientists at Alimama report that the system can produce up to 20,000 lines of copy per second.

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The ability to "Produce Smart Copy" — across a variety of ad formats for posters, web banners, headlines, and product pages — can make copywriting more efficient. Brands and companies using the service can even decide the tone of their copy. Alimama reports that users can choose between long or short, “promotional, fun, poetic, or heartwarming” pieces of copy. Here are some other ways that you can use AI tools to strengthen your business’ reach:

Content sentiment and content analysis

How can a computer determine a customer’s sentiment? Well, it’s a two-pronged process that uses AI for both content analysis and sentiment analysis.

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‍Content analysis is the objective and quantitative assessment of a text-based, visual, or aural message. Researchers analyze each message scientifically.

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‍Sentiment analysis is the science of interpreting and classifying a user’s emotions about a specific brand, product, or service by using text analysis. Generally, the analysis determines whether the sentiment is positive, negative, or neutral.

The power of the internet and its reach into our lives has offered a vast amount of analyzable content gleaned from blogs, social media, YouTube, and websites. The software can recognize patterns without human input.

Determine your keywords

For the strongest search engine optimization (SEO), the right keywords are essential. NLP tools, like the ones Rellify uses, can help you choose keywords that increase your site’s traffic and attract just the right audience — readers who will turn into leads and customers.

Develop your content marketing strategy

Using NLP-based AI tools, you can perform a content audit to review all of the content on your site.  Repeat this practice regularly — every two months or so — to ensure that your content is up-to-date. Review your posts on social media and web pages to determine how your target audience perceives your customer service.

Use a lead-generator chatbot

Familiarize yourself with this NLP/NLG tool; you may find that it’s a helpful utility. For example, a chatbot can help you identify potential prospects and garner their interest in your products or services by asking questions, conducting surveys, and offering quizzes.  When a user visits your site, the bot can learn why. Some businesses use the information users give to chatbots to complement their email efforts, strengthen the customer experience, and qualify leads by asking specific questions or offering specific tools, all of which can increase their return on investment (ROI).

Text classification

Text classification, in the context of NLP, refers to the task of automatically categorizing text documents or phrases into predefined categories or classes. This type of is technique used in NLP applications, including:

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• Sentiment analysis
• Spam detection
• Content tagging
• Topic categorization

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All of these are valuable use cases, but let's take a closer at a specific type of text classification employed by NLPs — topic categorization.

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Rule-based classification in topic categorization

This can be a super helpful tool, especially if your business deals with large volumes of data that would take a human much longer to comb through.

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Rule-based techniques use a set of manually constructed language rules to categorize text into groups. These rules tell the system to classify text into a particular category based on the content of a text by using semantically relevant text elements. An antecedent or pattern and a projected category make up each rule.

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For example, imagine you have tons of new articles, and your goal is to assign them to relevant categories such as Parenting, Health, School, etc.

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With a rule-based classification system, you will do a human review of a couple of documents to come up with linguistic rules like this one:

• If the document contains words such as doctor, wellness, remedies, or medicine, it belongs to the Health group (class).

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Rule-based systems can be refined over time and are understandable to humans. However, there are certain drawbacks to this strategy.

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These systems, to begin with, demand in-depth expertise in the field. They take a lot of time since creating rules for a complicated system can be difficult and frequently requires extensive study and testing.

The Power of Natural Language Processing

A digital transformation is taking place on a global scale, and you can use these NLP tools to strengthen your bottom line. Rellify seamlessly uses NLP and other Artificial Intelligence tools within its platform, allowing you to find topics and keywords that will resonate best with your target audience. Our AI capabilities can then help you write content that will naturally rank well on search engines. With an exclusive and custom Relliverse™, we use our NLP tools to crawl huge volumes of data specific to you, your competitors, and your market. Through machine learning and NLP, we'll help you create your own content or generate it automatically. The platform even uses AI to provide helpful guidance in SEO. Our "R-score" changes in real time as you write and edit to help you create relevant content.

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Wondering how it all works? Schedule a consultation or demo with a Rellify expert, free of charge, and find out exactly how Rellify can supercharge your content marketing efforts.