Natural Language Processing For Multilingual SEO

In 2014, Airbnb faced a challenge: its platform was growing globally, but the majority of its content was in English.

This meant users in countries like Japan or France weren’t getting the seamless, localized experience they expected.

Here’s where Airbnb made a pivotal choice:

Rather than relying on traditional translation methods, Airbnb embraced NLP-powered solutions to localize content for over 190 countries, leading to a significant increase in engagement from non-English-speaking users.

This example is no anomaly.

As more companies tap into international markets, the need to speak the language of your audience—literally—has become essential.

A staggering 72.1% of consumers spend most or all of their time on websites in their native language​(SEOptimer).

Despite this, many businesses still fail to address this language gap effectively.

With advanced AI models like Google’s BERT and MUM, search engines can now better understand and rank content in multiple languages, allowing companies to break language barriers and extend their reach beyond English-speaking audiences.

In this article, we’ll explore how natural language processing (NLP) is reshaping multilingual SEO and why optimizing for multiple languages is a must-have in today’s global marketplace.

How NLP Enhances Search Engines’ Understanding of Multilingual Content

Search engines no longer rely solely on matching keywords.

Instead, they’re getting smarter about understanding the actual meaning behind words, phrases, and queries—regardless of the language.

This transformation that pushed towards entity based SEO, is largely thanks to Natural Language Processing (NLP), which allows search engines like Google to interpret complex multilingual content with greater accuracy.

NLP Understands Semantics Beyond Translation

NLP goes beyond simple word-for-word translation by grasping the semantic meaning of text.

This means understanding the context in which words are used, even when the sentence structure or idiomatic expressions differ across languages.

A query in French like “acheter des chaussures en ligne” (buy shoes online) isn’t treated the same as its English counterpart; NLP recognizes not just the meaning of each word but the intent behind the search, ensuring the most relevant results are surfaced.

Google’s BERT model, which stands for Bidirectional Encoder Representations from Transformers, plays a critical role here.

By analyzing relationships between all words in a query, BERT helps Google understand the full context, not just the presence of individual keywords. This technology is especially impactful in languages where word order or grammar differs significantly from English​ (Oncrawl - Technical SEO Data).

NLP in Action: MUM and Cross-Language Understanding

Google’s MUM (Multitask Unified Model) takes this even further. MUM can understand and generate content across 75 different languages.

But its real strength lies in its ability to process information from multiple sources and deliver answers in any language, regardless of the language the query was made in​ (Search Engine Land).

For example, if a user in Italy searches in Italian for information that only exists in Chinese, MUM can understand the query and retrieve relevant content in Italian by leveraging cross-language insights.

This kind of multilingual understanding has massive implications for businesses.

Brands that optimize content across multiple languages no longer have to rely on separate SEO strategies for each region. Instead, they can leverage NLP-driven models like MUM to ensure their content is understood and ranked appropriately, no matter the language.

Case Study: Booking.com

Take Booking.com, another leader in global accommodation. The company leveraged BERT and MUM to ensure its listings and reviews were properly optimized for global users.

By allowing these models to interpret the nuances of different languages, Booking.com ensured that a search for a hotel in Madrid, whether made in English, Spanish, or Chinese, would return the most relevant local content.

This not only improved user experience but also increased organic traffic from non-English-speaking countries by over 15%​ (SEOptimer)​(Search Engine Land).

The Role of Entity Recognition and Sentiment Analysis

Another way NLP enhances search engines’ multilingual capabilities is through entity recognition. Entities are essentially the building blocks of NLP. They refer to specific people, places, or things that search engines recognize and use to match user intent with relevant content.

For instance, if someone searches for "meilleur restaurant à Paris" (best restaurant in Paris), NLP recognizes "restaurant" as an entity, "Paris" as a location, and "meilleur" as the intent behind the search, allowing for more accurate results.

Sentiment analysis is another layer that NLP brings to multilingual SEO. By understanding the tone or sentiment behind a piece of content, search engines can tailor the results to match the user’s emotional intent.

This is particularly useful when dealing with user-generated content like reviews, where tone matters. For instance, an overly negative review in Japanese should be interpreted the same way as one written in Spanish, and NLP ensures this consistency.

Airbnb uses sentiment analysis in reviews and listings across its platform, adjusting for the nuances in different languages. By doing so, it provides users with more relevant search results and improves customer satisfaction worldwide​(SEOptimer).

NLP Tools for Multilingual SEO

To fully leverage NLP in multilingual SEO, companies can use a range of tools to optimize their content:

Google Cloud Natural Language API: Provides advanced entity recognition, sentiment analysis, and syntax analysis across multiple languages.

IBM Watson NLP: Offers robust language processing for sentiment and intent analysis in multilingual content.

DeepL: A translation tool that uses NLP to provide more accurate, context-based translations compared to traditional models.

Growing Globally with NLP: Case Studies

Natural Language Processing (NLP) has played a massive role in helping businesses make global expansion more accessible than ever.

Here, we look at real-world case studies to understand in what way businesses leveraged NLP to grow globally with success.

IKEA – Localizing Product Descriptions for Global Markets

IKEA, one of the world’s leading furniture retailers, faced a massive challenge in ensuring that its product information was understandable and engaging across multiple regions. With operations in over 50 countries and a product catalog that spans thousands of items, the need for localized content became vital.

Using NLP-based multilingual SEO strategies, IKEA employed entity recognition and content localization to optimize product descriptions for different languages and cultures.

This wasn’t a simple translation of text, but rather a nuanced adjustment that took local cultural references and language structures into account.

While a Swedish description of a lamp might focus on minimalist design, an Italian version might highlight craftsmanship, tapping into what resonates with local audiences.

IKEA’s use of Google’s NLP tools helped ensure that each version of their website ranked highly for localized search queries, leading to a 20% increase in organic traffic from non-English-speaking regions​ (Search Engine Land)​(Oncrawl - Technical SEO Data).

Airbnb – Scaling with Cross-Language Search Capabilities

Airbnb has successfully scaled its platform to accommodate travelers from over 191 countries, offering listings in dozens of languages.

Rather than rely solely on manual translations, Airbnb adopted NLP-powered localization to ensure that descriptions, reviews, and user-generated content were optimized for users in their native languages.

They used sentiment analysis to adjust the tone and meaning of content, ensuring that reviews in Spanish, for instance, carried the same weight and emotional tone as those in English or Japanese.

By leveraging NLP’s ability to analyze context and intent across languages, Airbnb made it easier for users worldwide to find accommodations relevant to their needs.

The impact? Airbnb saw a 15% boost in international bookings after integrating NLP into their multilingual SEO strategy, ensuring that localized content ranked well in global search results​ (SEOptimer)​(Search Engine Land).

Booking.com – Cross-Language Entity Recognition

As a global leader in travel accommodations, Booking.com handles millions of listings across 43 languages.

The company employed advanced entity recognition and cross-language understanding tools powered by NLP.

By identifying key entities like “hotel,” “accommodation,” and “luxury,” in multiple languages, Booking.com ensured that search engines like Google could easily match user queries with relevant listings, even when the searcher’s language differed from the content language.

This was especially crucial for international users who may search in their native language but expect localized results.

Booking.com’s use of Google’s MUM and other NLP models resulted in a significant improvement in organic traffic from non-English-speaking users, leading to better user experiences and higher conversion rates globally​ (Search Engine Land)​(SEOptimer).

Unilever – NLP for Content Localization in Diverse Markets

Unilever, a global consumer goods company, markets its products across 190 countries. They implemented NLP-based localization for its web content and marketing campaigns.

Rather than just translating content, Unilever used NLP-powered tools to adjust for regional dialects, cultural context, and specific linguistic nuances.

Product descriptions for Dove or Lipton tea would vary depending on whether they were targeting a Spanish-speaking market in Latin America or an Arabic-speaking market in the Middle East.

NLP’s ability to discern and optimize these cultural and linguistic nuances enabled Unilever to improve its content’s relevance and ranking in local searches.

By optimizing content across different languages using NLP, Unilever saw a 15% improvement in user engagement from non-English-speaking regions ​(Search Engine Land).

Alibaba – Enhancing Multilingual Customer Support with NLP

Alibaba, a global e-commerce giant, serves customers from a wide variety of linguistic backgrounds.

They employed NLP-powered chatbots and support systems. These systems handle inquiries in multiple languages, translating queries, and providing localized responses.

With the use of NLP’s cross-language understanding, Alibaba ensured that the tone, context, and intent of customer queries were preserved, leading to more accurate responses.

This helped them enhance customer experience across global markets and contributed to a significant reduction in cart abandonment rates in non-English-speaking regions ​(Search Engine Land).

6 Best Practices for Implementing NLP in Multilingual SEO

Below are key best practices to guide you in using NLP to strategically focus on both search engines and user experience.

Use Structured Data for Language Markup

Structured data helps search engines understand the content of your pages, including which language version is intended for different users.

Implementing schema markup such as hreflang tags tells search engines which content should be shown based on the user’s location and language settings.

Why it matters: Without hreflang tags, search engines may show the wrong version of your website to users, leading to poor engagement rates.

Real-world example: Websites like IKEA and Booking.com use structured data to ensure that search engines display the appropriate language version to their international users, boosting rankings for localized queries​.

Focus on Local Keyword Research

When expanding your SEO strategy to other languages, simply translating your English keywords won’t suffice.

NLP enables you to perform local keyword research by identifying terms and phrases that native speakers use. Tool like Google Trends allows you to find search volume and intent across different languages.

Why it matters: Users in different regions search differently, even when searching for the same products. For example, while English speakers search for “buy sneakers online,” in Spain, they might search for “comprar zapatillas deportivas.”

Best practice: Use NLP-powered tools like Ahrefs or Google Keyword Planner to find long-tail keywords that resonate with local audiences ​(Oncrawl - Technical SEO Data) ​(SEOptimer).

Optimize Content for Local Search Engines

In some regions, Google may not be the dominant search engine. For instance, Baidu leads the Chinese market, and Yandex dominates in Russia. NLP can help optimize your content to meet the requirements of these local search engines, which often have different ranking algorithms and content preferences.

Why it matters: Neglecting local search engines can result in lost visibility and organic traffic from key international markets.

Pro tip: Use tools that provide insights into Baidu’s or Yandex’s SEO requirements, ensuring your content is optimized for regional search engines​ (Search Engine Land).

Create Content with Localization, Not Just Translation

One of the core functions of NLP in multilingual SEO is understanding the cultural nuances and context behind language.

Translation tools often fall short because they don’t account for regional differences. Localization ensures your content resonates with your audience, aligning with cultural expectations and idiomatic expressions.

Why it matters: Properly localized content increases user engagement and decreases bounce rates. For example, product descriptions or blog posts should cater to local preferences and traditions.

Real-world example: Unilever uses NLP to localize content for its Dove brand, adjusting messaging to suit the preferences of each country and region, leading to better SEO performance​.

Leverage NLP Tools for Sentiment and Intent Analysis

To better tailor content to different audiences, you should use NLP tools for sentiment and intent analysis. These tools allow you to understand how people in various languages and regions feel about a topic or product, giving you insights into how to adjust your content to better match their needs.

Why it matters: Sentiment analysis helps brands adjust their tone based on how users in different regions respond emotionally to content. For instance, customer reviews and feedback can be analyzed to improve products or services in specific markets.

Pro tip: Use tools like Google Cloud’s Natural Language API or IBM Watson NLP to gain insights into user intent and emotional tone across multiple languages​.

Monitor Performance and Iterate

SEO is never a “set it and forget it” endeavor.

As you implement NLP for multilingual SEO, regularly track performance through tools like Google Search Console and Google Analytics.

Monitor metrics like organic traffic, bounce rates, and conversion rates to see how each language version performs.

Why it matters: Continuous monitoring helps you identify which strategies work best in different regions, allowing you to refine your SEO approach.

Pro tip: Focus on metrics specific to each language version and compare them to identify opportunities for improvement. For instance, if your French version has higher bounce rates, it may indicate that the content doesn’t align with local search intent .

The Future of NLP in Multilingual SEO: AI and Beyond

The future of NLP in multilingual SEO is definitely poised to not disappoint.

Emerging AI technologies are not only enhancing how search engines understand languages but also how businesses can tailor their content to global audiences.

Below are the key future trends and developments in NLP and AI that will reshape multilingual SEO.

AI-Powered Large Language Models: Expanding Multilingual Capabilities

The development of Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) and Google’s MUM (Multitask Unified Model) is taking NLP to new heights.

These models are trained on vast amounts of multilingual data, allowing them to generate and understand human-like text across dozens of languages. LLMs can predict context and meaning with high accuracy, making them essential for businesses that want to automate the creation of localized content.

As mentioned previously, MUM operates in 75 languages, with plans to expand further.

Machine Translation vs. NLP-Driven Localization

While machine translation has been around for years, it’s no longer enough for businesses aiming for true global reach.

In the future, NLP-driven localization will dominate over basic translations by understanding cultural nuances, idioms, and context at a deeper level.

This means businesses won’t just translate words; they’ll adapt their entire messaging strategy for each specific audience.

Tools like DeepL are already leading the charge in this space, offering more accurate and context-aware translations compared to older systems like Google Translate​.

Expect future NLP tools to go even further, enabling real-time content localization that adjusts everything from product descriptions to marketing copy based on regional preferences.

The Rise of Real-Time Multilingual Content Generation

With AI-driven models like ChatGPT and MUM, real-time multilingual content generation will become a game changer for businesses.

AI-powered content generation tools are expected to reduce content creation time by 30-50% while maintaining high quality​ (Search Engine Land)​(SEOptimer).

Imagine having the ability to instantly create product descriptions, blog posts, or even entire marketing campaigns in multiple languages—without the need for human translation.

This real-time capability will allow companies to react faster to global trends without the traditional lag time of content translation and adaptation.

Multimodal AI: Combining Text, Video, and Images for SEO

Multimodal AI, which can process and understand multiple forms of content, such as text, images, and video, is set to revolutionize how we approach multilingual SEO.

Instead of optimizing separate pieces of content for different media formats, businesses will be able to create cohesive SEO strategies that integrate all types of content seamlessly.

Google’s MUM model can interpret a text query and combine it with images or videos to deliver a more comprehensive search result.

This ability will be particularly important for industries like e-commerce and travel, where customers may want both text-based reviews and visual information in their native language​.

AI-Enhanced Personalization for Multilingual Audiences

Search engines and content platforms will soon not only deliver results based on the user’s language but also consider cultural preferences, browsing history, and behavior patterns.

This kind of hyper-personalization will create a more intuitive and user-friendly search experience, tailored specifically to the individual.

Imagine a Spanish-speaking user searching for "best shoes for winter hiking." In addition to returning results in Spanish, AI would consider the user's previous searches, location, and even preferred shoe brands to serve the most relevant results.

This level of personalization, driven by AI, will be key to boosting user engagement and conversions.

Future Challenges: Bias and Accuracy in Multilingual AI

Although NLP and AI are tremendously capable, some challenges remain sneaking in.

Especially when it comes to bias and accuracy in multilingual contexts.

Language models are only as good as the data they’re trained on, and if they’re exposed to biased or incorrect data, that bias can be reflected in search results and content generation.

There have been instances where NLP models struggle to correctly interpret non-Western languages, leading to inaccurate translations or misinterpretation of user intent ​(Search Engine Land).

As we move forward, there will need to be significant investments in creating unbiased, culturally diverse datasets to improve the quality and fairness of NLP models.

AI-Driven Multilingual SEO is Your Competitive Advantage

The future of multilingual SEO is bright, with AI and NLP paving the way for faster, more accurate, and culturally relevant content creation.

Businesses that adopt these technologies early will be able to stay ahead of the competition by offering truly localized and personalized experiences for their global audience.

From real-time content generation to multimodal search results, the future of NLP in SEO is all about breaking down barriers—whether they be linguistic, cultural, or technological.

Think of it this way: while others are still painting with broad strokes, NLP allows you to craft a masterpiece with precision, capturing every nuance of language and culture.

It’s clear that in today’s era of content saturation, relying on simple translation is like showing up to a sword fight with a butter knife.

The future belongs to those who seek it: use advanced technologies like NLP to break down language barriers, deliver targeted experiences, and stay ahead of evolving search algorithms.