TF*IDF and the Evolution of Topic Models
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I review how tf*idf is used to help expand keyword and content research, to build deeper, more relevant pages to rank in organic search.
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TF*IDF and the Evolution of Topic Models
- 1. TF*IDF Introduction to Topic Modeling for SEO 24 Hours of SEO Prepared for January 25, 2018 Nick Eubanks Presented by @nick_eubanks
- 2. Hi, I’m Nick Eubanks ❏ First business (painting) at 19 years old. $280k in revenue during summer of 2003. ❏ Launch first digital company in 2008 (atomni), built CMS for rapid deployment of Microsites. Sold. ❏ Build custom CMS for reviews in Japan. Angel funded. Sold. ❏ Built and sold lead-gen websites in medical, legal, and SEO. ❏ Joined TrafficSafetyStore.com in May 2012. Grew from ~$3M to well north of ~$20 Million ❏ Currently split time between NK Tech and IFTF. Nick Eubanks Founder and CEO, I’m From The Future Founder, TrafficThinkTank.com Executive Director, NK Tech Owner, ADDHero.com @nick_eubanks
- 3. We All Want More Traffic to Our Website @nick_eubanks
- 4. Ideally traffic that is... ❏ Sustainable ❏ Scalable ❏ Free @nick_eubanks
- 6. Understanding Search Engines ❏ Process of information retrieval; NLP ❏ Training machines to “understand” text ❏ Identifying patterns; topics ❏ Identifying importance; term frequency @nick_eubanks
- 7. In Comes TF*IDF ❏ Term frequency x inverse document Frequency ❏ Process for machine-based information retrieval ❏ Calculates weights based on frequency of terms used throughout entire document set @nick_eubanks
- 8. What does that look like... ❏ Defining term “weights” based on the frequency of use through the document ❏ Removal of common “stop” words like the, and, is, because, and so on ❏ Excluding common HTML elements such as words used in header, footer, sidebars, and navigational elements @nick_eubanks
- 9. Topic Identification ❏ Identify themes across terms to group into “topics” ❏ Ahrefs has some of this ❏ Much better with trained corpora using tf*idf ❏ Can do manually if you have the patience ❏ Use topics to design your content calendars @nick_eubanks
- 10. Building Relevance ❏ Process: Keyword research > Topic Identification > Theme ❏ Pillar content = Top-level theme ❏ Build topic content in clusters ❏ Example: https://www.hubspot.com/marketing -statistics @nick_eubanks
- 11. Imagine this Scenario ❏ I gave you a stack of 1,000 documents ❏ Could you answer these questions: ❏ How would you organize them? ❏ What if I asked what they were about? ❏ What if I asked for all the documents about watermelons? ❏ How would you provide this information? @nick_eubanks
- 12. You would need to create a topic model ❏ To do this, you could use a set of different colored highlighters ❏ Then scan the documents looking for words that are related to each other, and highlight them ❏ Then you would create an index ❏ You would come up with a topic for each set of related terms ❏ You would annotate which pages terms relevant to that topic appeared on @nick_eubanks
- 13. This is How Search Engines Work @nick_eubanks
- 14. Example Topic: Organic Farming ❏ Fruit ❏ Watermelons ❏ Lemons ❏ Apples ❏ Vegetables ❏ Squash ❏ Lettuce ❏ broccoli ❏ Organic foods ❏ usda ❏ Lower yields ❏ Organic standards ❏ Farmers ❏ Organic farmers ❏ Certified organic ❏ Synthetic pesticides @nick_eubanks
- 15. TF*IDF for “Organic Farming” @nick_eubanks
- 16. Compared to #1 Ranking URL @nick_eubanks
- 17. Probabilistic Complexities ❏ Automating the identification of topics ❏ Validating topics and training your model ❏ Creation of word vectors @nick_eubanks
- 18. What are Word Vectors ❏ Simply, a vector of weights ❏ Word2Vec ❏ Pre-trained ❏ King - man + woman = queen ❏ Spatial relations where proximity represents relevance @nick_eubanks
- 19. Term Clustering into Topics using LDA ❏ Tf*idf has evolved - a look at latent dirichlet allocation [LDA] ❏ LDA is a generative statistical model ❏ Ability to generate probabilities; this is where it gets interesting... @nick_eubanks
- 21. Topic Clustering ❏ Grouping topics into themes ❏ Where keyword research becomes important ❏ Building corpora of documents representative of relevant topics ❏ Scraping Google really helps with this @nick_eubanks
- 22. How Does This Get You More SEO Traffic? @nick_eubanks
- 23. What if you knew... ❏ The terms Google was expecting to find ❏ The topics Google was expecting to find ❏ The concepts that should be linked to ❏ The concepts and content that your page should be linked from? @nick_eubanks
- 24. Not “Keyword Density” ❏ Dial in the term weights from tf*idf for a specific document ❏ Stuff in a bunch of keywords because you see them weighted heavily in the top ranking pages ❏ Google is much, much smarter than this ❏ Look for terms that represent topics ❏ Use those topics to perform additional keyword research ❏ Explore the pages currently ranking, and analyze their ❏ Internal links (both directions) ❏ External links (both directions) Don’t Do @nick_eubanks
- 25. Implementing Data from a Topic Model ❏ 28” orange traffic cones ❏ 18” lime traffic cones ❏ Portable traffic cones ❏ Yellow traffic cones ❏ Extendable traffic cone barricades Terms ❏ Orange traffic cones ❏ Lime traffic cones ❏ Colored traffic cones ❏ Collapsible traffic cones ❏ Cone bars ❏ Work zone safety Topics ❏ Traffic cones ❏ Safety cones ❏ Road safety cones Themes @nick_eubanks
- 26. @nick_eubanks
- 27. @nick_eubanks
- 28. @nick_eubanks
- 29. Looking at the URL Architecture ❏ Traffic Cones ❏ Orange and Lime Traffic Cones ❏ 28” Orange Traffic Cones ❏ 28” Lime Traffic Cones ❏ 18” Orange Traffic Cone ❏ Colored Traffic Cones ❏ Grabber Cones ❏ Cone Bars ❏ Collapsible Traffic Cones ❏ Traffic Cone Accessories Product Content ❏ Traffic Cones ❏ History of Traffic Cones ❏ Custom Traffic Cones ❏ Traffic Cone Selection Guide ❏ Traffic Cones for Construction @nick_eubanks
- 30. Implementing Data from a Topic Model ❏ Defining content type ❏ Designing content map Reference: imfromthefuture.com/content-map/ ❏ Laying out content calendar ❏ Designing an SEO-First URL Architecture ❏ Publishing ❏ Expanding keyword footprint Reference: imfromthefuture.com/bigfoot-strategy/ @nick_eubanks
- 31. Take TF*IDF For a Spin https://imfromthefuture.com/tfidf-embed/ @nick_eubanks
- 32. Take LDA Visualization For a Spin https://imfromthefuture.com/lda-tool/ @nick_eubanks