GPT-4 vs. Google Cloud: Performance comparison on 9 SEO tasks

Over the past year, OpenAI’s APIs, particularly the expansive language model GPT-4 and its chatbot counterpart ChatGPT, have garnered significant attention. Despite the longstanding availability of marketing APIs from industry giants like Google Cloud, Microsoft Azure, and AWS, many search marketers are increasingly favoring generative AI models for their SEO-related endeavors.

This article delves into a comparative analysis between the performance of GPT-4 and Google Cloud’s machine learning APIs, assessing their efficacy across common SEO tasks that benefit from automation. These tasks encompass semantic analysis, classification, translation, and image understanding.

While the scope of applications for these technologies in SEO and digital marketing is vast, the primary objectives of this analysis are to:

1. Spark creative thinking about the utilization of existing machine learning APIs in your daily operations.
2. Provide insights into various areas where machine learning-based automation can enhance efficiency and optimize outcomes for digital marketers.
3. Emphasize the significance of judiciously selecting automation tools based on the specific task at hand, as opposed to relying on a one-size-fits-all solution, such as ChatGPT, for all automation needs.

To evaluate the models’ efficacy, I’ve subjected them to a comparative analysis using identical datasets—comprising identical text, video/audio, or image content, depending on the specific task. This process has been replicated across three distinct data points for each task, enhancing the reliability of the insights presented in the subsequent comparative analysis.

Named Entity Recognition (NER) stands at the intersection of computer science and natural language processing (NLP), concentrating on the identification and classification of specific entities within textual content, including individuals’ names, organizations, and locations.

Entity analysis primarily aims to adeptly extract such details from unstructured text data or, more broadly, from free-form text.

Alternatively, unstructured text can undergo analysis through syntax analysis, a domain devoted to comprehending and dissecting sentence structures in a language. This aids machines in deciphering meaning from textual content.

Key facets of syntax analysis encompass:

1. Parsing Sentence Structure: Syntax analysis entails breaking down a sentence into its constituent elements, such as nouns, verbs, adjectives, etc. The objective is to comprehend how these elements combine to form grammatically correct sentences.
2. Dependency Parsing: It involves understanding the interdependencies among different words in a sentence—identifying main verbs, subjects, objects, and so forth.
3. Parts of Speech Tagging: This step entails assigning parts of speech to each word in a sentence, distinguishing whether a word functions as a noun, verb, adjective, etc. This categorization is pivotal for understanding each word’s role in a sentence.
4. Phrase Chunking: This phase groups words into naturally cohesive phrases, facilitating subsequent syntactic and semantic analyses.
5. Syntax Trees: Many syntax analysis tools generate a tree diagram (syntax tree) to illustrate the hierarchical structure of a sentence, elucidating relationships between various sentence components.

In the context of SEO, these approaches find utility in diverse projects, contributing to tasks such as enhancing content relevance, refining keyword strategies, and optimizing website structures for improved search engine visibility. NER and syntax analysis collectively empower SEO professionals to derive actionable insights from unstructured text, fostering more effective strategies and campaigns.

In the realm of SEO, entity recognition proves valuable across various projects:

1. SERP Analysis:
   • Utilize your keyword universe as a foundation for SERP analysis, leveraging tools like dataforSEO.
   • Employ a Sheets template to extract entities from top-ranking pages’ titles, URLs, or meta descriptions.
   • Insights garnered from this analysis inform the creation of topical maps and guide content direction.
2. Keyword Research:
   • Initiate keyword research and identify keywords containing entities.
   • Develop content maps centered around closely linked entities and compile lists of positive and negative secondary entities for content creation.
   • Evaluate keywords featuring entities in the knowledge graph.
3. Internal Linking Audits:
   • Begin with internal linking anchor text data, validating anchors that include entities.
   • Analyze anchor text for entities, especially those in the knowledge graph.
   • Utilize website content, obtained through crawling, along with internal links to identify pages mentioning entities and establish connections.
4. Competitor Content Analysis:
   • Extract entities from competitors’ website content, encompassing text, titles, and meta descriptions.
   • Include prominent entities in your content map if identified on competitors’ sites.
   • Examine the correlation between articles featuring prominent entities in titles/meta descriptions and their impact on traffic.
5. Social Comments Analysis:
   • Scrape user-generated comments from platforms like YouTube, TikTok, X, and Facebook.
   • Analyze these entities and map the findings against your site content for enriched insights.
   • Explore entity sentiment associated with the comments to uncover user sentiments, especially regarding the brand or its products/services, such as pricing.

In SEO, employing text analysis methods becomes imperative when you aim to:

1. Analyze Content at Scale:
   • Utilize text analysis methods in SEO to thoroughly examine and comprehend content on a large scale, whether it’s sourced from SERPs or competitor websites.
2. Identify N-grams:
   • Uncover n-grams within titles, meta descriptions, content, and anchor text. This exploration provides valuable insights into the composition and relevance of the content under scrutiny.
3. Determine Successful Title Structures:
   • Analyze existing content to discern effective title structures for your targeted keywords. Identify patterns and structures that resonate well with your audience.
4. Enhance Entity Analysis:
   • Improve content relevance and SEO performance by detecting the most common words associated with target entities and attributes. This enhances your understanding of how entities are portrayed in the digital landscape.
5. Facilitate Structured Data Automation:
   • Recognize repeated use of specific terms and combinations to trigger automated structured data JSON completion. For instance, if a page frequently mentions terms like “FAQ” or “Frequently asked questions,” initiate the extraction of FAQPage structured data. Similarly, for a page emphasizing “recipe” more than three times, trigger the completion of Recipe schema, and so forth.

These text analysis methods in SEO empower you to delve into the intricacies of content, revealing patterns, structures, and associations that can significantly impact your digital strategy.

Analyzing GPT-4 Against Google Cloud Natural Language API for Entity Extraction and Text Analysis: A Comparative Study

In this section, I will delve into two distinct machine learning methodologies for text grouping in the context of SEO analysis: clustering and classification. Both play pivotal roles in efficiently managing various tasks on a large scale.

Clustering, an unsupervised machine learning approach, revolves around partitioning an unlabeled dataset into groups of similar data objects.

On the other hand, classification, a supervised machine learning approach, entails sorting data objects into pre-defined categories using provided labels.

To elucidate the disparity between these two approaches, let’s consider a straightforward example from the SEO domain. Imagine having a list of pages (URLs) from a website that needs appropriate category tags based on their content:

• If you possess a predefined list of categories, where the content aligns with known criteria, you can employ a classification machine learning algorithm to analyze the content and assign labels based on the provided category list.
• Conversely, if you lack a predetermined list of categories, meaning the content’s nature is unknown, a clustering machine learning algorithm can be implemented. It analyzes the content, identifies key terms, and assigns the most suitable topic label, organizing each piece into topics while approximating their alignment to the identified topics.

These approaches find practical applications in SEO, with their significance stemming from their distinct functionalities:

• Classification is instrumental when there’s prior knowledge of content categories, facilitating accurate labeling based on predefined criteria.
• Clustering becomes invaluable when content lacks predetermined categories, offering a means to organize content into topics based on inherent similarities and providing an approximation of each piece’s alignment with these topics.

Explore the impact of classification on refining your SEO strategy or expediting certain processes by reviewing the following scenarios:

1. Quickly Understanding a Large Website’s Content:
   • Employ classification to rapidly discern the breadth of topics covered by a website’s content.
2. Content Gap Analysis or Competitor Research:
   • Utilize classification to assess if competitor sites’ content aligns with the intended business direction during content gap analysis or competitor research.

Consider the practical applications of clustering in SEO for enhanced strategy and efficiency:

1. Grouping Keywords in a Keyword Universe:
   • Employ clustering to categorize and comprehend various topics within an extensive keyword set.
2. Understanding Website Content without Prior Knowledge:
   • Leverage topic clusters, which operate on semantic or keyword extraction methods, to identify prominent topics and cluster content based on sub-topic similarity, aiding in comprehending a website’s content when the subject is unknown.

Content transformation involves altering content into varied formats, styles, or lengths. Its significance for SEO stems from omnipresence and accessibility considerations.

Omnipresence: Omnipresence underscores the necessity for your brand or content to exist across diverse forms and platforms. This need arises from:

• Customers’ desire for content consumption in different formats.
• Google’s ever-changing SERP preferences for various content types.

Crucially, having the same content in written and video formats doesn’t automatically qualify as duplicate for Google. Whether in blog posts or videos, diverse formats benefit users by offering flexibility and assist search engines in meeting diverse content preferences without considering it as duplicate.

Accessibility: Accessibility involves providing diverse ways for audiences to consume your content, expanding reach to a broader, more inclusive audience. Consider different platforms (YouTube, TikTok, Spotify, Instagram, Pinterest, Medium) and content formats (text, image, video, audio).

For instance, transforming textual content into audio caters to people with visual impairments or those who prefer listening over reading, ensuring your content is inclusive and accessible to a wider audience.

Text-to-text content transformation can enhance your organic reach in various scenarios:

1. Leveraging High-Performing Blogs:
   • Original Scenario: You have a high-performing blog or resource section but no presence on platforms like LinkedIn, X, or Facebook Groups.
   • Transformation: Grow your audience and brand reach by adapting the content to suit each platform’s unique requirements. Regularly publish insightful and helpful posts to potentially improve lead generation.
2. Revamping Email Newsletter Content:
   • Original Scenario: Your email newsletters have substantial content but limited engagement.
   • Transformation: Repurpose the newsletter content into visually appealing, bite-sized posts for social media or blogs. Incorporate interactive elements to boost reader engagement, encourage shares, and increase overall visibility and subscriber numbers.
3. Optimizing In-Depth Research:
   • Original Scenario: Your in-depth research or whitepapers are underutilized.
   • Transformation: Break down extensive research into smaller, digestible articles or blog posts. Each piece can focus on specific aspects or findings, maximizing the utilization of your comprehensive content.
4. Unlocking Gated Content Value:
   • Original Scenario: Comprehensive guides or reports in PDF format are only accessible through gated content.
   • Transformation: Extract key insights, summaries, or actionable tips from PDFs and repurpose them into blog posts, LinkedIn articles, or X threads. Provide a teaser of the value in the full PDF to drive traffic back to your gated content.

Unlock Organic Reach with Audio-to-Text or Video-to-Text Content Transformation:

1. Optimize YouTube Videos for Blogs:
   • If you possess a collection of high-performing YouTube videos without a corresponding blog, leverage audio transcription to swiftly launch and scale your blog presence.
2. Repurpose Webinars and Workshops:
   • Transform successful webinars or online workshops into a multi-part blog series or compile them into an ebook. This strategy extends the lifespan of your content, catering to an audience that prefers reading over video consumption or live events.
3. Revitalize Podcasts and Interviews:
   • Transcribe running podcasts or interview series and present them as text on landing pages. Incorporate structured data and links to the audio or video resources, enhancing accessibility. Additionally, infuse key insights or supplementary contributions for added value.

While machine learning models advance, it’s crucial to recognize that translation doesn’t currently substitute for localization.

For SEO or user-facing content, consider a content localization specialist as the final editor. Ideally, this specialist should also align the last draft with SEO research for the target market.

However, automated translation can be beneficial for medium to large websites or those on a constrained budget. It’s quicker, more cost-effective, and scalable compared to traditional translation methods.

Ditch the one-size-fits-all approach. While diving deep into GPT-4, don’t overlook the valuable tools offered by Google Cloud. Explore task-specific API applications for accelerated processes, enhanced enterprise-level work, and SEO career advancement. Leveraging APIs for distinct tasks can:

1. Accelerate the transformation of data into insights.
2. Elevate the quality of your work, especially when dealing with large-scale websites.
3. Foster career and skill growth as an SEO consultant.

Remember, each model comes with its own set of advantages and limitations for specific tasks. Prioritize research on the training and optimal use of each model before implementation.