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Using Free Machine Learning API's for SEO - #SMX Munich 2016

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Collect millions of reviews from travel websites, extract entities via AlchemyAPI and train a model to predict search behaviour in upcoming months based on what users are writing about specific geographical areas, specific accommodations? Or how about a recommendation engine for e-commerce platforms, that not only takes into account the number of purchases but also SEO specific factors like keyword difficulty, number of external links and more to find the right balance between internal linking and commercially interesting items? Classifying and structuring huge datasets of content can be time consuming, why not us a free trained Machine Learning API for Topic Detection to do this for you? In this session Jan Willem Bobbinck will introduce the concept of machine learning and share a few practical examples on how you can use it to optimize your SEO processes.

Published in: Data & Analytics

Using Free Machine Learning API's for SEO - #SMX Munich 2016

  1. 1. International Freelance SEO
  2. 2.
  3. 3. International Freelance SEO Brand Ambassador Majestic Cycling & Skating Science: Physics in particular http://www.cyclingacrosstheworld.com/
  4. 4. The field of
  5. 5. “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -Tom Mitchell, Carnegie Mellon University
  6. 6. E: 50 years of data about housing prices in Munich T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  7. 7. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. British mathematician and professor of statistics George E. P. Box that “all models are wrong, but some are useful”
  8. 8. Document Sentiment analysis of a specific URL: { "status": "OK", "url": " https://www.notprovided.eu/why-not-use-googles-wmt-data/ ", "totalTransactions": "1", "language": "english", "docSentiment": [ { "mixed": "1", "score": "0.412838", "type": "positive" } ] }
  9. 9. You know what you are looking for What do these datapoints have in common?
  10. 10. E: 50 years of data about housing prices in Munich T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  11. 11. No rules teached. It took Google’s AI thousands of games to detect losing was probably bad
  12. 12. http://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
  13. 13. No Free Lunch Theorem
  14. 14. Never test your classifier on your input data. Always keep at least 10% of available training data for testing and evaluation purposes
  15. 15. https://www.udacity.com/course/viewer#!/c-ud120/l-2254358555/m-2374468553
  16. 16. Best to start with: • https://www.coursera.org/learn/machine-learning by Andrew Ng (Baidu, former Google Brain) • Tom Mitchell lectures: http://www.cs.cmu.edu/~tom/10601_fall2012/lect ures.shtml • https://work.caltech.edu/telecourse.html Caltech ML course
  17. 17. http://pdf.th7.cn/down/files/1312/machine_learning_for_hackers.pdf
  18. 18. Mainly use pre trained models: – Spam classification of user generated content (comments & reviews) – Content classification – Text extraction from pages
  19. 19. • Query classification • Recommendation engines: internal linking based on both e-commerce, user behaviour and SEO metrics.
  20. 20. http://blog.mashape.com/list-of-50- machine-learning-apis/
  21. 21. • No NLP or Machine Learning knowledge is required. • Lot’s of pre trained models & you can train your own models
  22. 22. Machine Learning based scraping,Yeah!
  23. 23. https://www.notprovided.eu/7-tools-web-scraping-use- data-journalism-creating-insightful-content/
  24. 24. 1. Collected all hotel reviews 2. Check sentiment and main entities 3. Upload search volume and e-commerce data per hotel 4. Update internal linking accordingly
  25. 25. 1. Collected all hotel reviews 2. Plotted against time 3. Extract upcoming entities and sentiments 4. Predict future search behaviour 5. Create landingpages for future targeting
  26. 26. How about using Machine Learning
  27. 27. Tip: Check both the homepage and the specific link page!
  28. 28. Input: a URL -> output: plain text
  29. 29. • A list of links containing – Content language – Content topic – Spam probability – Content sentiment (if wanted) – Prioritized on language relevancy
  30. 30. • 10.000+ keywords? Use a ML classifier • Check for entities like places for local • Buying intent vs informational
  31. 31. Persona Customer journey stage Page Type Local identifier Tag Keyword Leisure NL Awareness Product Yes Campingaz Campingaz Munich Leisure NL Awareness Informational No terrasverwarmer Leisure NL Awareness Informational No terrasverwarming Leisure NL Awareness Informational No BBQ gasbarbecue Leisure NL Awareness Informational No BBQ gas bbq Leisure NL Consideration Informational No Generic gasfles Leisure NL Retention Informational No Generic gasfles vullen Leisure NL Retention Informational No Branded primagaz Leisure NL Consideration Informational No Generic gasfles kopen B2B-industrie Awareness Informational No LNG lng Leisure NL Consideration Product No Generic gasflessen Leisure NL Awareness Informational No Generic kookplaat gas Energie Awareness Informational No Propaan propaan Leisure NL Awareness Informational No Butaan butaan
  32. 32. "I liked the book you gave me yesterday, but the rest of my day was terrible."
  33. 33. { "summarized_data": “Mallorcan roads are well maintained, cyclist are really welcome and I really enjoyed it last year...", "auto_gen_ranked_keywords": [ "flight", "madrid", "mallorca", "training", "food", "plane", "delayed", "weather", "broken", "quest", "hot", "spirit", "horror", "booked", "hour", "wifi", "trip", "situation", "airport", "gate", "mallorcan", "lounge", "spend", "minute", "ve", "cyclist", "rainy", "missed", "netherland", "enjoyed", "road" ] }
  34. 34. • Facial recognition after account creation
  35. 35. Aw! Yes, said Miss Skinlin she hasn’t the first heir to the female figure. The waves dance bright and happy when I forgot to learn, before which she told me to read and study. My Uncle, with a commanding, What are you better than Kintuck. 19th century American literature http://blog.algorithmia.com/2015/12/nanogenmo-text-analysis-with-algorithmias/
  36. 36. 1. Input topic & Scrape current content 2. Create all N-grams 3. Create individual paragraphs 4. Randomly combine and create texts 5. Run through topic and sentiment classifiers to evaluate
  37. 37. https://algorithmia.com/algorithms/lizmrush/GenerateParagraphFromTrigram
  38. 38. • Restructure website content based on a set taxonomy of topics • Extract texts from top 30 and define text requirements (eg. Searchmetrics module) • Purchase prediction for new queries
  39. 39. • Use Google Tensorflow to identify image contents • Crawl topic related content • Generate automatic descriptions and paragraph text • Build a image library site including text, good for SEO  https://databricks.com/blog/2016/01/25/deep-learning-with-spark-and-tensorflow.html
  40. 40. • From 2011: Google Prediction API http://cloudacademy.com/blog/google-prediction-api/
  41. 41. https://www.quora.com/Machine-Learning/How- do-I-learn-machine-learning-1

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