{"id":11779,"date":"2026-03-25T20:51:52","date_gmt":"2026-03-25T20:51:52","guid":{"rendered":"https:\/\/www.vappingo.com\/word-blog\/?p=11779"},"modified":"2026-04-06T15:28:27","modified_gmt":"2026-04-06T15:28:27","slug":"amazon-ads-keyword-research-books","status":"publish","type":"post","link":"https:\/\/www.vappingo.com\/word-blog\/amazon-ads-keyword-research-books\/","title":{"rendered":"Keyword Research for Amazon Book Ads"},"content":{"rendered":"<p><!-- VAPPINGO \u00b7 C5 \u00b7 5.9 \u00b7 \/amazon-ads-keyword-research-books\/ --><\/p>\n<article class=\"vap-art\">\n<div style=\"background:#0c0c0f;border-radius:16px;padding:52px;margin-bottom:48px;\">\n<div style=\"margin-bottom:24px;\"><span style=\"display:inline-flex;align-items:center;padding:5px 14px;background:rgba(50,186,211,.12);border:1px solid rgba(50,186,211,.35);border-radius:20px;font-size:10px;font-family:'DM Mono','Courier New',monospace;letter-spacing:2px;color:#32BAD3;font-weight:500;text-transform:uppercase;\">Amazon Ads \u00b7 Vappingo<\/span><\/div>\n<div style=\"font-size:clamp(22px,3.5vw,36px);font-weight:800;letter-spacing:-1px;line-height:1.1;color:#f2f0f5;margin:0 0 18px;font-family:'DM Sans',sans-serif;\">Amazon Ads Keyword Research: <em style=\"font-style:normal;color:#32BAD3;\">Building Your Initial List Before Launch<\/em><\/div>\n<p style=\"font-size:16px;color:#c8c4d8;line-height:1.8;max-width:560px;margin:0 0 28px;padding:0;\">Before your automatic campaign generates real data, you need a keyword list good enough to launch a productive manual campaign from day one. This guide covers every research method available to KDP authors \u2014 from competitor ASIN research to trope vocabulary mining \u2014 and how to combine them into a structured launch list.<\/p>\n<div style=\"border-top:1px solid #32323f;padding-top:20px;\">\n<table style=\"border-collapse:collapse;width:auto;background:transparent;\">\n<tbody>\n<tr>\n<td style=\"padding:0 28px 0 0;white-space:nowrap;vertical-align:middle;border:none;background:transparent;\"><span style=\"font-family:'DM Mono','Courier New',monospace;font-size:11px;color:#9490a8;\">13-minute read<\/span><\/td>\n<td style=\"padding:0;white-space:nowrap;vertical-align:middle;border:none;background:transparent;\"><span style=\"font-family:'DM Mono','Courier New',monospace;font-size:11px;color:#9490a8;\">Beginner &middot; Intermediate<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n<\/div>\n<nav class=\"vap-toc\" aria-label=\"Article contents\">\n<div class=\"vap-toc-label\">Contents<\/div>\n<ol>\n<li><a href=\"#kw-why\">Why keyword research before launch matters<\/a><\/li>\n<li><a href=\"#kw-sources\">The six keyword research sources<\/a><\/li>\n<li><a href=\"#kw-asin\">Competitor ASIN keyword research<\/a><\/li>\n<li><a href=\"#kw-autocomplete\">Amazon autocomplete mining<\/a><\/li>\n<li><a href=\"#kw-categories\">Category and browse node terms<\/a><\/li>\n<li><a href=\"#kw-tropes\">Trope and reader vocabulary<\/a><\/li>\n<li><a href=\"#kw-author\">Author comparison keywords<\/a><\/li>\n<li><a href=\"#kw-str\">Search Term Report as a research source<\/a><\/li>\n<li><a href=\"#kw-rufus\">How Rufus AI affects keyword strategy in 2026<\/a><\/li>\n<li><a href=\"#kw-structure\">Organising your keyword list by type<\/a><\/li>\n<li><a href=\"#kw-size\">How many keywords you actually need<\/a><\/li>\n<li><a href=\"#kw-mistakes\">Keyword research mistakes at launch<\/a><\/li>\n<\/ol>\n<\/nav>\n<p>Most authors launching their first manual Amazon Ads campaign have the same problem: they know they need a keyword list, but they do not know where to get one. Amazon&#8217;s console suggests a few keywords automatically, genre terms feel obvious but vague, and the thought of building a list from scratch is daunting. The result is usually a small list of generic terms that generates poor performance and confirms the author&#8217;s suspicion that manual targeting does not work. The issue is not the campaign structure \u2014 it is the research that preceded it.<\/p>\n<p>Good pre-launch keyword research produces a manual campaign that generates meaningful data from day one, rather than sitting nearly dormant waiting for automatic campaign results to arrive. This guide covers every research source available.<\/p>\n<h2 id=\"kw-why\">Why Keyword Research Before Launch Matters<\/h2>\n<p>Your automatic campaign will eventually surface converting keywords \u2014 but eventually means 14\u201330 days minimum. During those first weeks, a manual campaign seeded with poor keywords is either burning budget on irrelevant matches (if you use broad match without good research) or generating near-zero impressions (if your keyword pool is too small and too specific). Neither situation is useful.<\/p>\n<p>Good pre-launch research means your manual campaign has 40\u201380 well-researched, genre-appropriate keywords from day one. It generates meaningful impressions immediately, produces early conversion data that tells you which keyword themes resonate, and supplements your automatic campaign&#8217;s discovery rather than waiting passively for it. The first 30 days of advertising for a well-researched manual campaign look substantially better than those for a poorly researched one \u2014 which matters both for your ACoS and for the algorithm learning that influences your ongoing quality score.<\/p>\n<h2 id=\"kw-sources\">The Six Keyword Research Sources<\/h2>\n<p>For book advertising specifically, the most productive research sources are: competitor ASIN keyword analysis, Amazon autocomplete, browse category terminology, reader trope vocabulary, author comparison terms, and \u2014 once you have campaigns running \u2014 your own Search Term Report. The first five are pre-launch sources. The sixth is a continuous, real-time improvement layer.<\/p>\n<h2 id=\"kw-asin\">Competitor ASIN Keyword Research<\/h2>\n<p>This is the single highest-value pre-launch research method for book advertising, and it is available through the Book Keyword Spy tool at <a href=\"https:\/\/app.vappingo.com\" target=\"_blank\" rel=\"noopener\">KDP Rank Fuel by Vappingo<\/a>. Enter any comparable book&#8217;s ASIN and see every keyword that book ranks for in Amazon&#8217;s organic search results \u2014 the full vocabulary profile of a book your readers would also consider buying.<\/p>\n<p>Why this works: Amazon&#8217;s bestselling books in your genre are bestselling because readers find them through search. The keywords they rank for are the terms readers demonstrably use when searching for that type of book. If a direct competitor consistently ranks for &#8220;amateur sleuth cosy mystery England,&#8221; that keyword has proven real demand from your target audience. Targeting it in your manual campaign is not guesswork \u2014 it is leveraging the competitive landscape as a research investment you did not have to make yourself.<\/p>\n<p>Process: identify 10\u201315 directly comparable books in your category using the Competition Analyzer tool. Choose a mix of breakout recent titles (for current reader vocabulary trends) and established bestsellers (for high-volume, consistently searched terms). Run the Book Keyword Spy on each. Across 10\u201315 ASINs, you will typically surface 200\u2013400 distinct keywords. Filter for relevance \u2014 remove any that clearly do not apply to your specific book&#8217;s genre, subgenre, or audience.<\/p>\n<p>What remains is your highest-confidence keyword list: terms proven to drive readers to comparable books, filtered for your specific book&#8217;s applicability. These terms are your phrase and exact match core from day one.<\/p>\n<h2 id=\"kw-autocomplete\">Amazon Autocomplete Mining<\/h2>\n<p>Amazon&#8217;s search bar autocomplete is driven by real user search volume data. Every suggestion Amazon shows is a term that large numbers of real Amazon readers have searched recently. This makes it one of the most accurate available windows into actual reader search behaviour \u2014 not hypothesised behaviour, but observed patterns from Amazon&#8217;s own data.<\/p>\n<p>Process: go to Amazon.co.uk (or your target marketplace) and type your primary genre term into the search bar without pressing Enter. Record all autocomplete suggestions. Then type each of those suggestions, and record their autocomplete suggestions. Systematically work through your primary subgenre terms, trope terms, and series-type phrases in the same way.<\/p>\n<p>For &#8220;cosy mystery,&#8221; Amazon&#8217;s autocomplete typically surfaces &#8220;cosy mystery books,&#8221; &#8220;cosy mystery series,&#8221; &#8220;cosy mystery books UK,&#8221; &#8220;cosy mystery British,&#8221; &#8220;cosy mystery with recipes,&#8221; &#8220;cosy mystery with cats,&#8221; &#8220;cosy mystery short stories&#8221; \u2014 each a distinct audience segment with its own demand signal. &#8220;Cosy mystery with cats&#8221; tells you there is a meaningful subset of cosy mystery readers who specifically want cat characters \u2014 if your book has a cat, that term belongs on your keyword list immediately.<\/p>\n<p>Autocomplete mining for 10\u201315 seed terms typically produces 80\u2013150 candidate keywords in an hour. Many will overlap; many will reveal audience segments you had not specifically considered.<\/p>\n<h2 id=\"kw-categories\">Category and Browse Node Terms<\/h2>\n<p>The names of Amazon&#8217;s browse categories in your book&#8217;s genre area are reader-facing search terms that Amazon explicitly associates with your type of book. If your book belongs in &#8220;Crime, Thrillers &amp; Mystery &gt; Crime Fiction &gt; Cosy Mystery,&#8221; every level of that hierarchy is a keyword your readers may search: &#8220;crime fiction,&#8221; &#8220;crime thrillers mystery,&#8221; &#8220;cosy mystery&#8221; \u2014 and importantly, all the adjacent categories: &#8220;amateur sleuth,&#8221; &#8220;women sleuths,&#8221; &#8220;culinary mysteries,&#8221; &#8220;village mysteries.&#8221; These terms are not guesses \u2014 Amazon built its browse structure around how readers actually search and categorise books.<\/p>\n<p>Find comparable categories by browsing Amazon&#8217;s Books &gt; Categories &gt; Mystery, Thriller &amp; Suspense hierarchy (or your equivalent). Note the full names of every category that could apply to your book. These category-level terms have high search volume and moderate competition \u2014 good volume to start generating data quickly. Add them as phrase match keywords.<\/p>\n<h2 id=\"kw-tropes\">Trope and Reader Vocabulary<\/h2>\n<p>Readers \u2014 particularly romance and fantasy readers \u2014 have developed rich, specific trope vocabulary that they actively search by. This vocabulary is often dramatically different from what an author would naturally think to target. Authors think in genre terms (&#8220;contemporary romance&#8221;); readers think in trope terms (&#8220;forced proximity office romance slow burn&#8221;).<\/p>\n<p>Trope vocabulary examples by genre:<\/p>\n<p><strong>Romance:<\/strong> enemies to lovers, forced proximity, fake dating, second chance romance, grumpy sunshine, slow burn, sports romance, small town romance, billionaire romance, friends to lovers<\/p>\n<p><strong>Fantasy:<\/strong> dark academia, cosy fantasy, romantasy, fae romance, chosen one, found family fantasy, portal fantasy, progression fantasy, litrpg<\/p>\n<p><strong>Mystery:<\/strong> cosy mystery, amateur sleuth, armchair detective, locked room mystery, village mystery, culinary mystery, tea shop mystery<\/p>\n<p><strong>Science fiction:<\/strong> solarpunk, hopepunk, first contact, generation ship, hard sci-fi, space opera, cli-fi, near-future thriller<\/p>\n<p>These trope terms are underused by most book advertisers \u2014 CPCs are typically lower than broad genre terms, and conversion rates are often higher because the reader searching a specific trope is expressing highly specific intent. A romance reader searching &#8220;grumpy sunshine slow burn&#8221; who finds your book and clicks has already self-identified as exactly your target reader \u2014 they have done the relevance filtering themselves before the click.<\/p>\n<p>Where to find your genre&#8217;s current trope vocabulary: reader communities on Goodreads (particularly the reading challenge threads), BookTok and Bookstagram hashtag collections, Reddit&#8217;s r\/suggestmeabook and genre-specific subreddits, and reader review language on Amazon itself (what words appear repeatedly in five-star reviews of comparable books?).<\/p>\n<h2 id=\"kw-author\">Author Comparison Keywords<\/h2>\n<p>Readers regularly search Amazon for &#8220;books like [author name]&#8221; or &#8220;[author name] books&#8221; when they want more of a reading experience similar to a favourite author. For KDP authors, targeting comparable traditionally published authors \u2014 particularly breakout or critically acclaimed names in your specific subgenre \u2014 captures readers who are explicitly in the market for what you offer.<\/p>\n<p>&#8220;Books like Richard Osman&#8221; is a real search term generating real Amazon traffic from cosy mystery readers. &#8220;Books similar to M.C. Beaton&#8221; targets Hamish Macbeth fans. &#8220;Books like Jodi Picoult&#8221; targets emotional contemporary fiction readers. &#8220;If you like Stephen King&#8221; targets horror readers. These searches have high purchase intent \u2014 the reader is explicitly asking for a recommendation in a genre they already love.<\/p>\n<p>Research method: identify 10\u201320 comparable published authors who write in the same specific subgenre as your book. For each, check Amazon autocomplete for &#8220;[author name] books,&#8221; &#8220;books like [author name],&#8221; &#8220;similar to [author name],&#8221; &#8220;authors like [author name].&#8221; Add the patterns that emerge as phrase match keywords.<\/p>\n<p>Important restriction: do not use trademarked book or series names (e.g., &#8220;Twilight,&#8221; &#8220;Harry Potter&#8221;) as keywords \u2014 this can violate Amazon&#8217;s advertising policies and risk campaign rejection. Author names are generally acceptable; branded series titles are not.<\/p>\n<h2 id=\"kw-str\">Search Term Report as a Research Source<\/h2>\n<p>Once your automatic campaign has run for 14+ days, the Search Term Report is your most valuable and most accurate research source \u2014 because it is not research at all, it is evidence. Real searches, real clicks, real conversion data for your specific book in your specific marketplace.<\/p>\n<p>The entire harvest-and-scale workflow described in our <a href=\"https:\/\/www.vappingo.com\/word-blog\/search-terms-report-amazon-ads\/\">Search Term Report guide<\/a> is essentially keyword research conducted through live advertising spend. The terms you harvest are not candidates \u2014 they are proven converters. Treat the Search Term Report as your ongoing research engine and your pre-launch research as the starting investment that produces data quickly enough to begin the real research cycle.<\/p>\n<h2 id=\"kw-rufus\">How Rufus AI Affects Keyword Strategy in 2026<\/h2>\n<p>Amazon&#8217;s Rufus AI shopping assistant, which has been integrated into the main Amazon app and website experience since 2024, conducts semantic search rather than keyword matching. When a reader asks Rufus &#8220;what should I read if I liked Richard Osman but want something a bit darker?&#8221; it does not match keyword strings \u2014 it reads your full listing, reviews, and reader responses to assess contextual fit.<\/p>\n<p>For keyword strategy, this means two things. First, your product page content and reviews now influence your ad relevance in new ways \u2014 Rufus-mediated discovery pulls from description language and review vocabulary, not just your backend keywords. Second, conversational search terms (&#8220;books to read on holiday by the sea,&#8221; &#8220;something cosy to read in winter&#8221;) increasingly surface in the Search Term Report as Rufus integration deepens. These long-form, conversational terms tend to have low CPCs and high purchase intent \u2014 they represent a new category of keyword worth targeting explicitly in phrase match campaigns.<\/p>\n<p>The practical adaptation: write your description with reader vocabulary in mind \u2014 the words your readers use to describe the reading experience they want, not just the words that describe your book&#8217;s plot. Monitor your Search Term Report for emerging conversational patterns in search terms and add them to your phrase match ad group as they appear.<\/p>\n<h2 id=\"kw-structure\">Organising Your Keyword List by Type<\/h2>\n<p>Structure your keyword list into thematic groups before entering it into campaigns. This makes campaign organisation much cleaner and reveals whether you have coverage across all the keyword types your genre requires.<\/p>\n<p>Recommended groupings: Genre\/subgenre terms (broad category language), Trope terms (specific reader vocabulary), Setting\/period terms (particularly useful for historical fiction and location-specific novels), Author comparison terms, Series-type terms (&#8220;complete series,&#8221; &#8220;long series to binge&#8221;), Mood\/atmosphere terms (&#8220;cosy read,&#8221; &#8220;light-hearted mystery,&#8221; &#8220;feel-good fiction&#8221;), and Format-specific terms (if relevant: &#8220;standalone mystery,&#8221; &#8220;series starter,&#8221; &#8220;novella&#8221;).<\/p>\n<p>In your manual campaign, create separate ad groups for each major thematic cluster. This keeps performance data clean per theme \u2014 if your trope terms are outperforming your genre terms, you can see that clearly and adjust bids independently rather than managing everything from one mixed pot.<\/p>\n<h2 id=\"kw-size\">How Many Keywords You Actually Need<\/h2>\n<p>A common question: how large should the initial keyword list be? The honest answer: 40\u201380 keyword phrases across your manual campaign&#8217;s ad groups is a solid starting position for most books. More than this in the first 30 days risks spreading budget too thin to generate meaningful data on any individual term. Fewer than 30 risks insufficient impressions volume to generate usable data.<\/p>\n<p>Quality matters more than quantity. 40 highly relevant, well-researched phrase match keywords will consistently outperform 200 loosely relevant broad match keywords in generating usable data. The goal is not to be everywhere \u2014 it is to be exactly in front of the right readers with enough budget concentration to generate conversion signals.<\/p>\n<p>After 60\u201390 days of Search Term Report harvesting, your effective keyword list grows organically from real data. The pre-launch list is a starting point, not a permanent structure.<\/p>\n<h2 id=\"kw-mistakes\">Keyword Research Mistakes at Launch<\/h2>\n<p><strong>Targeting broad genre terms only.<\/strong> &#8220;Mystery,&#8221; &#8220;romance,&#8221; and &#8220;fantasy&#8221; have enormous search volume but high CPCs and mixed intent. They will generate impressions and some clicks, but conversion rates are poor because the traffic is too broad. Specific subgenre and trope terms consistently outperform them.<\/p>\n<p><strong>Using only the keywords Amazon suggests in the setup wizard.<\/strong> Amazon&#8217;s suggested keywords are a starting point derived from your metadata, not a comprehensive research output. They represent a small fraction of the available keyword landscape for your book.<\/p>\n<p><strong>Adding keywords without match types in mind.<\/strong> Adding 80 keywords all as broad match will generate massive noise. Plan your match type distribution: primarily phrase match, a smaller set of exact match for your highest-confidence terms, and a carefully limited broad match group with active management.<\/p>\n<p><strong>Not checking competitor keyword rankings before launch.<\/strong> Bypassing the ASIN research step means your initial keyword list is entirely theoretical \u2014 vocabulary you think readers use rather than vocabulary proven readers demonstrably use for comparable books. The ASIN research step typically produces the 20\u201330 most valuable keywords in your entire initial list.<\/p>\n<p><strong>Treating keyword research as a one-time activity.<\/strong> Reader vocabulary evolves, new tropes emerge, new comparable authors break through. The initial research gets you started. The Search Term Report keeps you current.<\/p>\n<p>The Keyword Goldminer tool at <a href=\"https:\/\/app.vappingo.com\" target=\"_blank\" rel=\"noopener\">KDP Rank Fuel by Vappingo<\/a> generates 500 related search terms for any seed keyword, profit-scored by search volume and competition \u2014 a comprehensive starting resource that cuts your pre-launch research time significantly. The Amazon Ads Generator tool produces a complete campaign structure including keyword lists, match types, starting bids, and negative keywords from a single book description input.<\/p>\n<p>When your well-researched campaigns deliver readers to your product page, the quality of the product they find determines conversion. <a href=\"https:\/\/www.vappingo.com\/Proofreading-Services\/Manuscript-Proofreading-Services\">Professional manuscript proofreading from Vappingo<\/a> ensures your book earns the reviews that both convert future ad clicks and improve your quality score in the auction.<\/p>\n<div style=\"background:#1c1c22;border-radius:12px;padding:32px;margin-top:52px;\">\n<p style=\"font-family:'DM Mono','Courier New',monospace;font-size:10px;letter-spacing:2px;text-transform:uppercase;color:#32BAD3;margin:0 0 20px 0;padding:0;\">Continue Reading &middot; Amazon Ads<\/p>\n<table style=\"width:100%;border-collapse:collapse;table-layout:fixed;\">\n<tbody>\n<tr>\n<td style=\"width:50%;padding:0 5px 10px 0;vertical-align:top;height:1px;\">\n        <a href=\"https:\/\/www.vappingo.com\/word-blog\/amazon-ads-keyword-match-types\/\" style=\"display:block;background:#252530;border:1px solid #32323f;border-radius:8px;padding:15px 17px;text-decoration:none;height:100%;box-sizing:border-box;\"><br \/>\n          <span style=\"display:block;font-family:'DM Mono','Courier New',monospace;font-size:9px;letter-spacing:1.5px;text-transform:uppercase;color:#32BAD3;margin-bottom:7px;\">Match Types<\/span><br \/>\n          <span style=\"display:block;font-size:13.5px;font-weight:600;color:#c8c4d8;line-height:1.4;font-family:'DM Sans',sans-serif;\">Keyword Match Types: Exact, Phrase, and Broad for KDP<\/span><br \/>\n        <\/a>\n      <\/td>\n<td style=\"width:50%;padding:0 0 10px 5px;vertical-align:top;height:1px;\">\n        <a href=\"https:\/\/www.vappingo.com\/word-blog\/search-terms-report-amazon-ads\/\" style=\"display:block;background:#252530;border:1px solid #32323f;border-radius:8px;padding:15px 17px;text-decoration:none;height:100%;box-sizing:border-box;\"><br \/>\n          <span style=\"display:block;font-family:'DM Mono','Courier New',monospace;font-size:9px;letter-spacing:1.5px;text-transform:uppercase;color:#32BAD3;margin-bottom:7px;\">Reports<\/span><br \/>\n          <span style=\"display:block;font-size:13.5px;font-weight:600;color:#c8c4d8;line-height:1.4;font-family:'DM Sans',sans-serif;\">How to Use the Search Terms Report Every Week<\/span><br \/>\n        <\/a>\n      <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>Amazon Ads \u00b7 Vappingo Amazon Ads Keyword Research: Building Your Initial List Before Launch Before your automatic campaign generates real data, you need a keyword list good enough to launch a productive manual campaign from day one. This guide covers every research method available to KDP authors \u2014 from competitor ASIN research to trope vocabulary &#8230; <a title=\"Keyword Research for Amazon Book Ads\" class=\"read-more\" href=\"https:\/\/www.vappingo.com\/word-blog\/amazon-ads-keyword-research-books\/\" aria-label=\"More on Keyword Research for Amazon Book Ads\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31],"tags":[],"class_list":["post-11779","post","type-post","status-publish","format-standard","hentry","category-kdp-publishing"],"_links":{"self":[{"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/posts\/11779","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/comments?post=11779"}],"version-history":[{"count":2,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/posts\/11779\/revisions"}],"predecessor-version":[{"id":11793,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/posts\/11779\/revisions\/11793"}],"wp:attachment":[{"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/media?parent=11779"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/categories?post=11779"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vappingo.com\/word-blog\/wp-json\/wp\/v2\/tags?post=11779"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}