Building an Identity Extraction Engine

2. Premise You can determine the personality profile of a person based on their browsing habits

3. Technology was the Solution!

4. Then I Read This… Us & Them The Science of Identity By David Berreby

5. The Different States of Knowledge What a person knows What a person knows they don’t know What a person doesn’t know they don’t know

6. Technology was NOT the Solution Identity and discovery are NOT a technology solution

7. Our Subject Material

8. Our Subject Material HTML content is unstructured You can’t trust that anything semantically valid will be present There are some pretty bad web practices on the interwebz

9. How We’ll Capture This Data Start with base linguistics Extend with available extras

11. The Basic Pieces Keywords Without all the fluff Weighting Word diets FTW Page Data Scrapey Scrapey

12. Capture Raw Page Data Semantic data on the web is sucktastic Assume 5 year olds built the sites Language is the key

13. Extract Keywords We now have a big jumble of words. Let’s extract Why is “and” a top word? Stop words = sad panda

14. Weight Keywords All content is not created equal Meta and headers and semantics oh my! This is where we leech off the work of others

16. Questions to Keep in Mind Should I use regex to parse web content? How do users interact with page content? What key identifiers can be monitored to detect interest?

17. Fetching the Data: The Request The Simple Way $html = file_get_contents('URL'); The Controlled Way $c = curl_init('URL');

18. Fetching the Data: cURL $req = curl_init($url); $options = array( CURLOPT_URL => $url, CURLOPT_HEADER => $header, CURLOPT_RETURNTRANSFER => true, CURLOPT_FOLLOWLOCATION => true, CURLOPT_AUTOREFERER => true, CURLOPT_TIMEOUT => 15, CURLOPT_MAXREDIRS => 10 ); curl_setopt_array($req, $options);

19. //list of findable / replaceable string characters $find = array('/\r/', '/\n/', '/\s\s+/'); $replace = array(' ', ' ', ' '); //perform page content modification $mod_content = script>#is', '', $page_content); $mod_content = preg_replace('#<style(.*?)>(.*?)</ style>#is', '', $mod_content); preg_replace('#<script(.*?)>(.*?)</ $mod_content = strip_tags($mod_content); $mod_content = strtolower($mod_content); $mod_content = preg_replace($find, $replace, $mod_content); $mod_content = trim($mod_content); $mod_content = explode(' ', $mod_content); natcasesort($mod_content);

20. //set up list of stop words and the final found stopped list $common_words = array('a', ..., 'zero'); $searched_words = array(); //extract list of keywords with number of occurrences foreach($mod_content as $word) { $word = trim($word); if(strlen($word) > 2 && !in_array($word, $common_words)){ $searched_words[$word]++; } } arsort($searched_words, SORT_NUMERIC);

21. Scraping Site Meta Data //load scraped page data as a valid DOM document $dom = new DOMDocument(); @$dom->loadHTML($page_content); //scrape title $title = $dom->getElementsByTagName("title"); $title = $title->item(0)->nodeValue;

22. //loop through all found meta tags $metas = $dom->getElementsByTagName("meta"); for ($i = 0; $i < $metas->length; $i++){ $meta = $metas->item($i); if($meta->getAttribute("property")){ if ($meta->getAttribute("property") == "og:description"){ $dataReturn["description"] = $meta->getAttribute("content"); } } else { if($meta->getAttribute("name") == "description"){ $dataReturn["description"] = $meta->getAttribute("content"); } else if($meta->getAttribute("name") == "keywords”){ $dataReturn[”keywords"] = $meta->getAttribute("content"); } } }

24. Weighting Important Data Tags you should care about: meta (include OG), title, description, h1+ , header Bonus points for adding in content location modifiers

25. Weighting Important Tags //our keyword weights $weights = array("keywords" => "3.0", "meta" "header1" "header2" => "2.0", => "1.5", => "1.2"); //add modifier here if(strlen($word) > 2 && !in_array($word, $common_words)){ $searched_words[$word]++; }

26. Expanding to Phrases 2-3 adjacent words, making up a direct relevant callout Seems easy right? Just like single words Language gets wonky without stop words

27. Working with Unknown Users The majority of users won’t be immediately targetable Use HTML5 LocalStorage & Cookie backup

28. Adding in Time Interactions Interaction with a site does not necessarily mean interest in it Time needs to also include an interaction component Gift buying seasons see interest variations

29. Grouping Using Commonality Common Interests Interests User A Interests User B