{"id":1869,"date":"2026-09-05T05:01:27","date_gmt":"2026-09-05T05:01:27","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/how-ai-measures-brand-sentiment\/"},"modified":"2026-09-05T05:01:27","modified_gmt":"2026-09-05T05:01:27","slug":"how-ai-measures-brand-sentiment","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/how-ai-measures-brand-sentiment\/","title":{"rendered":"Brand Sentiment Analysis: How AI Scores What People Say"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2>The Knowledge Gap in Brand Sentiment Measurement<\/h2>\n<p>Most brand teams know AI sentiment analysis exists, yet many lack a clear view of how it works or what the scores really mean. That gap produces surface-level numbers that arrive too late to guide action and offer no explanation for what caused the shift.<\/p>\n<p>This guide walks through the full pipeline from data collection through NLP, classification, and scoring. It also explains why social sentiment and AI search sentiment operate as two different data worlds that require distinct measurement approaches. For brands that need to move past surface-level scores and uncover the reasons behind them, <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Listen Labs<\/a> combines AI-moderated interviews with quantitative tracking to reveal the \u201cwhy\u201d behind every score.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<h2>Brand Sentiment Analysis in Plain Language<\/h2>\n<p>Brand sentiment analysis uses AI to determine the emotional tone behind mentions of a brand across digital channels. Tools classify each mention as positive, negative, or neutral. <a href=\"https:\/\/therankmasters.com\/insights\/brand-monitoring\/best-tools-for-brand-sentiment-analysis\" target=\"_blank\" rel=\"noindex nofollow\">Modern systems use transformer-based language models that read entire passages instead of isolated words<\/a>. This approach improves accuracy on sarcasm, irony, and mixed opinions.<\/p>\n<p>Three core use cases drive adoption across enterprise brand teams, and each solves a different business problem.<\/p>\n<ul>\n<li><strong>Brand health:<\/strong> Tracks how perception shifts over time and flags emerging risks before they become crises, protecting long-term brand value.<\/li>\n<li><strong>Campaign effectiveness:<\/strong> Shows whether marketing efforts actually change consumer perception, not just awareness or clicks.<\/li>\n<li><strong>Competitive positioning:<\/strong> Benchmarks sentiment against competitors to highlight relative strengths and vulnerabilities in the category.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/surveymonkey.com\/learn\/marketing\/brand-sentiment\" target=\"_blank\" rel=\"noindex nofollow\">Brand sentiment is a real-time, quantitative measure of expressed emotion<\/a>. It differs from brand reputation, which reflects long-term accumulated perception, and from brand perception, which captures stable beliefs consumers hold about a brand.<\/p>\n<p>To understand how these scores appear on dashboards, it helps to walk through the underlying pipeline.<\/p>\n<h2>How AI Systems Turn Conversations into Sentiment Scores<\/h2>\n<ol>\n<li> <strong>Data Collection<\/strong>\n<p>AI sentiment tools pull text from many sources. These include social platforms such as X, Instagram, TikTok, and LinkedIn, review sites like Google Business Profile, Amazon, and Yelp, forums such as Reddit, news articles, customer surveys, support tickets, and AI search engine outputs. <a href=\"https:\/\/stealthagents.com\/research\/ai-sentiment-analysis-statistics-2026\" target=\"_blank\" rel=\"noindex nofollow\">Enterprise platforms process over 500,000 tickets daily, and global tools analyze 4.6 billion social media posts per day.<\/a> Source selection shapes the story. A brand selling through retail partners may focus on review platforms, while a B2B software company needs LLM-specific tracking.<\/p>\n<p>Raw text then moves through preprocessing steps such as tokenization, part-of-speech tagging, and dependency parsing. Named Entity Recognition (NER) identifies brand, product, and competitor mentions. <a href=\"https:\/\/zealousys.com\/blog\/ai-sentiment-analysis-in-social-media-monitoring\" target=\"_blank\" rel=\"noindex nofollow\">Transformer models like BERT read text bidirectionally and capture context and nuance that keyword systems miss.<\/a><\/p>\n<p>Three main approaches dominate sentiment classification. Rule-based systems rely on predefined word lists tagged with emotional values. They are fast and interpretable but struggle with sarcasm and context. Machine learning classifiers train on labeled datasets and <a href=\"https:\/\/lorikeetcx.ai\/articles\/sentiment-analysis-practitioners-guide\" target=\"_blank\" rel=\"noindex nofollow\">reach 80\u201385% accuracy on standard benchmarks.<\/a> Hybrid approaches combine rules with ML and route ambiguous cases to human review.<\/p>\n<p>Aspect-Based Sentiment Analysis (ABSA) adds another layer. <a href=\"https:\/\/zealousys.com\/blog\/ai-sentiment-analysis-in-social-media-monitoring\" target=\"_blank\" rel=\"noindex nofollow\">A review saying \u201cthe app is incredibly fast but the pricing is unreasonable\u201d is classified positive on \u201cperformance\u201d and negative on \u201cpricing,\u201d<\/a> which produces more actionable guidance than a single overall label. <a href=\"https:\/\/techscience.com\/CMES\/v147n2\/67504\/html\" target=\"_blank\" rel=\"noindex nofollow\">LLMs show strong zero-shot and few-shot performance, reducing the need for task-specific labeled data, while fine-tuned smaller models still compete well on benchmarks.<\/a><\/p>\n<p>Most tools express sentiment on a polarity scale from \u22121 to +1 or roll it into a Net Sentiment Score (NSS) from \u2212100 to +100. Advanced systems detect specific emotions such as joy, anger, frustration, and surprise using frameworks like Ekman\u2019s universal emotions. <a href=\"https:\/\/zealousys.com\/blog\/ai-sentiment-analysis-in-social-media-monitoring\" target=\"_blank\" rel=\"noindex nofollow\">Treating all mentions equally distorts reputation signals; weighting scores by reach and engagement produces more useful guidance than raw mention counts.<\/a><\/p>\n<h2>Five Metrics That Complete the Sentiment Picture<\/h2>\n<p>Five metrics together provide a complete picture of brand sentiment. <a href=\"https:\/\/bizdevstrategy.com\/measuring-brand-sentiment\" target=\"_blank\" rel=\"noindex nofollow\">Tracking only a single score is the most common mistake in brand perception measurement.<\/a> These five metrics are:<\/p>\n<ul>\n<li><strong>Net Sentiment Score (NSS):<\/strong> <a href=\"https:\/\/cairrot.com\/blog\/brand-sentiment-tracking\" target=\"_blank\" rel=\"noindex nofollow\">Calculated as (Positive Mentions \u2212 Negative Mentions) \/ Total Mentions \u00d7 100.<\/a> <a href=\"https:\/\/getfoundquick.com\/how-to-track-brand-sentiment-in-ai-search-results\" target=\"_blank\" rel=\"noindex nofollow\">A score above +40 suggests healthy sentiment, while a score below 0 points to a specific problem.<\/a> NSS usually serves as the primary KPI for marketing teams, yet formulas vary across tools, so scores only compare cleanly when the underlying calculation is known.<\/li>\n<li><strong>Share of Voice:<\/strong> Compares a brand\u2019s mention volume and sentiment with competitors in the same category. This view shows whether the brand is gaining or losing ground in consumer conversations.<\/li>\n<li><strong>Theme Breakdown:<\/strong> Separates attributes such as pricing, ease of use, reliability, and customer support. This breakdown reveals which features drive positive or negative perceptions and reflects ABSA applied at scale.<\/li>\n<li><strong>Emotion Scores:<\/strong> Capture specific emotional states beyond simple polarity. These scores show the intensity and flavor of feeling behind a sentiment movement.<\/li>\n<li><strong>Sentiment Velocity:<\/strong> <a href=\"https:\/\/pulsarplatform.com\/guides\/how-to-measure-brand-sentiment-shift-2026\" target=\"_blank\" rel=\"noindex nofollow\">Measures the rate of change in sentiment over a defined period.<\/a> This metric acts as a leading indicator that flags emerging crises before static scores shift visibly.<\/li>\n<\/ul>\n<p>These metrics behave differently depending on whether the data comes from social posts or from AI search engines, so channel context matters as much as the numbers.<\/p>\n<h2>AI Search Sentiment and Social Sentiment as Separate Channels<\/h2>\n<p>Social sentiment captures what people post publicly on social media, reviews, and forums. It reflects reactive sharing of experiences, opinions, and complaints. Data in this channel is public, persistent, and searchable.<\/p>\n<p>AI search sentiment captures what AI assistants such as ChatGPT, Google AI Overviews, Gemini, and Perplexity say about a brand when buyers ask questions. <a href=\"https:\/\/ai-semantica.com\/blog\/ai-tools-score-brand-sentiment-chatgpt-responses\" target=\"_blank\" rel=\"noindex nofollow\">These responses are private to a single user and shaped by the prompt, conversation history, and the model\u2019s training data.<\/a><\/p>\n<p><a href=\"https:\/\/semrush.com\/blog\/ai-sentiment-analysis-marketers-guide\" target=\"_blank\" rel=\"noindex nofollow\">A Semrush survey of 1,030 US shoppers found that 57% used AI to narrow choices, 53% to compare considered products, and 50% to make a final decision.<\/a> A single negative mention in an AI response can remove a brand from consideration before it even appears on a short list.<\/p>\n<p>The two channels differ in four critical ways: data source, persistence, weight, and methodology.<\/p>\n<ul>\n<li><strong>Data source:<\/strong> Social sentiment passively monitors existing human conversations. AI search sentiment requires active querying of LLMs with standardized prompts.<\/li>\n<li><strong>Persistence:<\/strong> Social posts remain visible over time. <a href=\"https:\/\/trysight.ai\/blog\/brand-sentiment-analysis-in-llms\" target=\"_blank\" rel=\"noindex nofollow\">LLM responses are ephemeral and probabilistic, so the same prompt can yield different answers across sessions and models.<\/a><\/li>\n<li><strong>Weight:<\/strong> <a href=\"https:\/\/ai-semantica.com\/blog\/ai-tools-score-brand-sentiment-chatgpt-responses\" target=\"_blank\" rel=\"noindex nofollow\">A negative statement in an AI response often carries more weight than a negative social post because users treat it as objective and authoritative.<\/a><\/li>\n<li><strong>Methodology:<\/strong> AI search sentiment often uses a five-level classification scale of recommended, positive framing, neutral listing, hedged, and negative instead of a simple positive, negative, or neutral label.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/trysight.ai\/blog\/brand-sentiment-analysis-in-llms\" target=\"_blank\" rel=\"noindex nofollow\">Traditional social listening tools are mismatched for LLM sentiment analysis because LLM responses are private, ephemeral, and conversational, while social posts are public, persistent, and searchable.<\/a><\/p>\n<p>To see how your brand is framed across both social and AI search channels, <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">see conversational tracking in action<\/a> and uncover the reasons behind every sentiment shift.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<h2>Where Sentiment Scores Go Wrong<\/h2>\n<p>Sentiment scores provide directional signals rather than precise emotional readouts. Several known limitations reduce reliability in real-world conditions.<\/p>\n<ul>\n<li><strong>Sarcasm and irony:<\/strong> <a href=\"https:\/\/lorikeetcx.ai\/articles\/sentiment-analysis-practitioners-guide\" target=\"_blank\" rel=\"noindex nofollow\">Sarcasm can cause up to a 50% drop in sentiment analysis accuracy, and even frontier LLMs like GPT-4 can trail fine-tuned smaller models on sarcasm detection.<\/a><\/li>\n<li><strong>Negation handling:<\/strong> <a href=\"https:\/\/lorikeetcx.ai\/articles\/sentiment-analysis-practitioners-guide\" target=\"_blank\" rel=\"noindex nofollow\">Phrases like \u201cnot bad at all\u201d contain negative words but express mildly positive sentiment, and negation handling remains a difficult NLP problem.<\/a><\/li>\n<li><strong>Cultural and multilingual differences:<\/strong> <a href=\"https:\/\/pulsarplatform.com\/guides\/how-to-measure-brand-sentiment-shift-2026\" target=\"_blank\" rel=\"noindex nofollow\">Applying an English model to translated or non-English content introduces systematic errors in sarcasm, idioms, and cultural context.<\/a><\/li>\n<li><strong>Domain drift:<\/strong> <a href=\"https:\/\/lorikeetcx.ai\/articles\/sentiment-analysis-practitioners-guide\" target=\"_blank\" rel=\"noindex nofollow\">Models trained on product reviews behave differently on support tickets, and English-only models struggle with code-switching and regional expressions.<\/a> A <a href=\"https:\/\/ojphi.jmir.org\/2026\/1\/e80824\" target=\"_blank\" rel=\"noindex nofollow\">2026 study comparing five AI sentiment tools with human coding on COVID-19 public health datasets found poor concordance, with Cohen\u2019s kappa below 0.5 for all tools and sentiment categories.<\/a><\/li>\n<li><strong>Sample bias:<\/strong> <a href=\"https:\/\/bizdevstrategy.com\/measuring-brand-sentiment\" target=\"_blank\" rel=\"noindex nofollow\">Social media sentiment overrepresents vocal minorities because dissatisfied customers post more often than satisfied ones.<\/a><\/li>\n<li><strong>The \u201cwhy\u201d gap:<\/strong> A Net Sentiment Score shows that a number moved without explaining the cause. Because traditional trackers catch the drop but cannot explain it, by the time a KPI declines the underlying shift has already been building for months.<\/li>\n<\/ul>\n<p>These limitations point to a set of practices that keep sentiment measurement grounded and useful.<\/p>\n<h2>Turning Sentiment Scores into Reliable Signals<\/h2>\n<p>Combine quantitative scores with qualitative follow-up so the numbers always connect to real explanations. Sentiment scores act as directional signals, and open-ended conversations reveal the reasoning behind them.<\/p>\n<p>Use consistent question frameworks over time to keep trend data comparable. <a href=\"https:\/\/amicited.com\/blog\/how-sentiment-scoring-works-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Switching models, tools, or prompts mid-analysis breaks comparability and makes before-and-after numbers misleading.<\/a><\/p>\n<p>Validate AI findings with regular human review. <a href=\"https:\/\/amicited.com\/blog\/how-sentiment-scoring-works-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Sample scored examples monthly, have a human classify the same set, and measure agreement. If human-model agreement falls below an acceptable threshold, revisit the model.<\/a><\/p>\n<p>Segment sentiment by audience and source to avoid averages that hide risk. <a href=\"https:\/\/surveymonkey.com\/learn\/marketing\/brand-sentiment\" target=\"_blank\" rel=\"noindex nofollow\">A brand that looks fine on aggregate can mask a sharply negative segment that a blended score never reveals.<\/a><\/p>\n<p>Track sentiment velocity alongside static scores. <a href=\"https:\/\/trysight.ai\/blog\/sentiment-analysis-for-brand-mentions\" target=\"_blank\" rel=\"noindex nofollow\">A move from 0.4 to 0.6 over three months shows positive momentum, while a decline from 0.7 to 0.5 calls for immediate attention.<\/a> Weight sentiment by reach and influence so a negative post from a major creator or news outlet counts more than several minor complaints.<\/p>\n<h2>Choosing Tools That Explain the \u201cWhy,\u201d Not Just the Score<\/h2>\n<p>Social listening platforms such as Sprout Social, Brandwatch, and Meltwater track what people say across social media, reviews, and forums. They excel at volume and breadth and usually stop at polarity scores. These tools show that sentiment moved without explaining the underlying drivers.<\/p>\n<p>AI search monitoring tools such as OtterlyAI, Profound, and Semrush measure what LLMs say about brands in response to buyer queries. They cover the growing AI search channel but focus only on AI-generated text.<\/p>\n<p>Research platforms that go deeper add the qualitative layer missing from pure listening tools. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Listen Labs<\/a> combines AI-moderated interviews with quantitative tracking to uncover the reasons behind sentiment shifts, not just the scores themselves. Its Emotional Intelligence feature analyzes tone of voice, word choice, and subconscious micro-expressions, using Ekman\u2019s universal emotions framework, to surface feelings that transcripts alone miss. Listen Pulse is a conversational tracker that runs the same study wave after wave. It understands open-ended answers, sorts them into themes, and quantifies them. It then charts each theme next to the KPIs you already report.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<p>One well-known clothing brand, famous for its big logos, was quietly losing customers. Its old tracker recorded the drop without an explanation. Pulse revealed that price was not the issue. Style was the problem. A growing group of customers felt the big logos were too loud for their changing lifestyles. That difference between a number and an answer shaped the brand\u2019s next move.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Can ChatGPT do sentiment analysis?<\/h3>\n<p>ChatGPT and other large language models can perform sentiment analysis with strong zero-shot performance, meaning they classify sentiment accurately without task-specific training data. However, responses can vary across runs because of stochastic inference. Performance on sarcasm detection often lags behind fine-tuned smaller models, and LLMs lack the structured, traceable pipeline of dedicated sentiment tools. For brand monitoring at scale, a purpose-built system with consistent prompts, stored evidence snippets, and a fixed scoring rubric produces more reliable trend data than ad hoc LLM queries.<\/p>\n<h3>What are the three main types of sentiment analysis?<\/h3>\n<p>The three primary types are fine-grained analysis, aspect-based sentiment analysis (ABSA), and emotion detection. Fine-grained analysis classifies sentiment on a spectrum from very positive to very negative instead of a simple three-way split. ABSA identifies sentiment toward specific attributes such as price, quality, or customer support instead of producing a single overall label. Emotion detection identifies specific emotions like joy, anger, frustration, or surprise instead of only polarity. Each type answers a different question. Fine-grained analysis shows how strongly people feel. ABSA shows what they feel strongly about. Emotion detection shows the specific emotional state driving the response.<\/p>\n<h3>How is AI sentiment analysis different from traditional surveys?<\/h3>\n<p>Surveys capture stated opinions through preset questions with no ability to probe deeper. AI sentiment analysis processes unstructured data such as social posts, reviews, support tickets, and conversational responses at scale. This approach uncovers unexpected insights and emotional nuance that structured surveys miss. A deeper limitation of surveys is the say-do gap, where what people say and what they do diverge. A participant might report preferring human customer service, then click the AI agent in three seconds. Sentiment analysis of behavioral and conversational data catches contradictions that self-reported survey responses cannot.<\/p>\n<h3>What is the Net Sentiment Score formula?<\/h3>\n<p>The standard formula is NSS = (Positive Mentions \u2212 Negative Mentions) \/ Total Mentions \u00d7 100, which produces a score from \u2212100 to +100. Some tools include neutral mentions in the denominator, while others exclude them. This difference means NSS figures from different platforms are not directly comparable unless the underlying formula is known. For AI search sentiment, some tools extend the formula to include hedged mentions as a negative weight and neutral listings as a mild positive, reflecting the commercial impact of cautious AI framing on buyer decisions.<\/p>\n<h3>What is aspect-based sentiment analysis?<\/h3>\n<p>Aspect-based sentiment analysis (ABSA) identifies sentiment toward specific attributes of a brand or product instead of producing a single overall score. A restaurant review that praises the food but criticizes the long wait would be classified positive on food quality and negative on service, which produces more actionable guidance than document-level scoring. ABSA is particularly valuable for brand teams because it connects sentiment directly to operational decisions. A negative score on \u201ccustomer support\u201d points to a different fix than a negative score on \u201cpricing.\u201d Transformer-based models, particularly BERT and its variants, currently represent the state of the art for ABSA tasks.<\/p>\n<h2>Conclusion: Pairing Scores with Real Explanations<\/h2>\n<p>Understanding how AI measures brand sentiment, from data collection through NLP, classification, and scoring, creates a solid foundation. Numbers alone still fall short. As noted in the limitations, traditional tools catch drops without revealing their causes, and by the time a KPI declines the underlying shift has often been building for months.<\/p>\n<p>The strongest value from AI sentiment measurement comes from adding qualitative depth to quantitative scores. Teams need to know why a number moved, which audience drove the shift, and what they were reacting to. That qualitative layer often determines whether insights lead to action.<\/p>\n<p>Ready to move beyond the score and understand the \u201cwhy\u201d behind your customers\u2019 feelings? <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Talk to our team about a demo<\/a> and see how conversational tracking closes the gap.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-brand-sentiment-analysis\" target=\"_blank\">AI Brand Sentiment Analysis: What It Measures &amp; How<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/measure-brand-perception-ai-conversations\" target=\"_blank\">How to Measure Brand Perception Through AI Conversations<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-brand-sentiment-analysis-tools\" target=\"_blank\">AI Brand Sentiment Analysis Tools: Top Platforms Compared<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/how-ai-measures-brand-perception\" target=\"_blank\">How AI Measures Brand Perception: Metrics &amp; Audit Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-customer-brand-perception\" target=\"_blank\">Measure Customer Brand Perception with AI in 5 Steps<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Go beyond the score. Listen Labs breaks down AI brand sentiment\u2014key metrics, common pitfalls, and tools that explain the &#8220;why.&#8221; Measure smarter today.<\/p>\n","protected":false},"author":52,"featured_media":1868,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/comments?post=1869"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1869\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/1868"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=1869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=1869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=1869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}