Ad Sentiment: Brandwatch Q3 2026 Ad Campaign Insights

Click-through rates tell you *if* someone acted, but ad sentiment and customer perception tell you *why*, and how they felt about it. Getting a read on how your campaigns actually make people feel is what builds brand affinity and real purchase intent. If you’re only looking at clicks, you’re missing the part of the story that explains if your messages are connecting or just generating empty engagement.

Step 1: Setting Up Social Listening for Ad Sentiment Tracking

To get a handle on post-ad sentiment, you need to establish a solid social listening framework. It’s all about capturing the emotional tone and the context around your advertising. For this tutorial, we’ll use Brandwatch Consumer Research, a major platform for this kind of work, updated for its 2026 interface. It’s got what you need for sentiment detection and thematic analysis.

1.1 Create a New Project and Define Your Ad Campaign Queries

First, log into your Brandwatch account. In the left-hand navigation, hit Projects, then Create New Project. Name it something clear, like “Q3 2026 Ad Campaign Sentiment.”

Next up are your search queries, which tell Brandwatch what to listen for. Head to Data Sources & Queries and click Add Query. You’ll want to build specific queries for each ad campaign, or maybe even for individual ad creatives you’re testing. Get your queries right, because poorly constructed ones will bury you in irrelevant data.

1. Keyword Inclusion: You’ve got to include your brand name, any campaign hashtags, specific ad taglines, and unique product names from your ads. For example: "YourBrandName" AND ("#CampaignHashtag" OR "AdTagline" OR "ProductX").
2. Platform Specification: If your ads are running heavy on Instagram and TikTok, focus your listening there to cut down on the noise. Under Source Selection, just uncheck the platforms you don’t care about.
3. Competitor Context (Pro Tip): I always set up a separate query for key competitors using their campaign keywords. This gives you a benchmark for your own ad sentiment against what’s happening in the market. Something like: "CompetitorBrand" AND ("#CompetitorCampaign" OR "CompetitorProduct").

Common Mistake: Using keywords that are way too broad. This just pulls in a ton of useless data and makes any real sentiment analysis a nightmare. Be specific to what’s actually in your ad.

Once this is done, you’ll have a steady stream of mentions related to your ad campaigns, all categorized by platform and ready for you to start digging in.

1.2 Configure Sentiment Analysis Settings

As soon as your queries are running, Brandwatch starts applying its sentiment analysis engine. But you can, and should, refine it to be more accurate for your specific brand and industry. Go to Settings > Sentiment in your project.

1. Sentiment Model Selection: Brandwatch has a few pre-trained models. The “General” model is a decent place to start for most advertising. If your industry uses a lot of weird jargon, you might want to look into training a “Custom” model.
2. Rule-Based Sentiment: This feature is where you can really fine-tune things. Click Add Rule. For instance, if your brand name shows up in comments that seem negative but are actually positive (like, “I hated my old phone, but YourBrandName is amazing!”), you can build a rule. You could specify that if “YourBrandName” appears near words like “amazing” or “love,” the sentiment should always be tagged as positive.
3. Negative Term Exclusions: Make sure that common terms in your field that sound negative aren’t tripping up the system. If you’re in finance, “bear market” is a technical term, not someone expressing negative feelings about your investment ad.

Pro Tip: You have to regularly check a sample of the automatically classified mentions (they’re under Data > Mentions) and manually fix any that are wrong. I make it a point to review 100-200 mentions a week for any active campaign. This feedback loop trains the AI, making it smarter over time.

Doing this gives you much more accurate sentiment classifications (positive, negative, neutral) for your ad mentions, creating a reliable foundation for analysis.

Step 2: Analyzing Sentiment Data and Identifying Key Themes

With your social listening configured, it’s time to dive into the data to pull out insights about how people are actually perceiving your ads. This analysis needs to go deeper than just counting up the positives and negatives.

2.1 Explore Sentiment Trends in Dashboards

Inside your Brandwatch project, go to Dashboards. You’ll see pre-built dashboards that show you sentiment over time. The widgets to watch are “Sentiment Share,” “Sentiment Trend,” and “Top Negative/Positive Mentions.”

1. Sentiment Share by Ad Creative: If you created separate queries for different ads, build a widget that compares their sentiment scores. An ad with way lower positive sentiment or a spike in negative comments needs an immediate look.
2. Spikes and Dips: Keep an eye on the “Sentiment Trend” graph. Do you see a sudden jump in negative sentiment right after an ad launched? That correlation can pinpoint a problem with the creative or targeting. On the flip side, a sustained rise in positive sentiment after an ad goes live is a good sign your messaging is working.
3. Volume vs. Sentiment (Editorial Aside): Don’t get hypnotized by sentiment percentages alone. A campaign might have 80% positive sentiment, which sounds great, but if that’s from only 50 mentions, its actual impact is tiny. I’d much rather have a campaign with 60% positive sentiment from 5,000 mentions. That shows you have the reach from the volume and the connection from the sentiment.

Common Mistake: Focusing only on the overall sentiment score and ignoring the context. A high positive percentage from a tiny, niche audience doesn’t mean the ad will have broad appeal.

The goal here is a clear visual read on how sentiment for your ads is shifting which lets you spot winning campaigns and ones that need a fix, fast.

2.2 Conduct Thematic Analysis on Sentiments

Brandwatch’s Topics feature is great because it automatically groups your mentions into themes, which gives you the *why* behind the sentiment. Go to Topics in your project.

1. Auto-Categorization: Brandwatch uses AI to find recurring themes. Look at what it generates. For ads, you’ll probably see themes pop up like “product features,” “brand values,” “ad tone,” or “customer service.”
2. Manual Topic Creation: Sometimes the AI misses things specific to your ad, like a character or a new jingle. You can create topics manually. Just click Add Topic and define the keywords for it. So if your ad has a unique song, create a topic to catch all mentions of it.
3. Sentiment by Topic: Once you have your topics, you can filter to see the sentiment for each one. Is the sentiment for “product features” positive, but the sentiment for “ad tone” is negative? That’s a huge tell. It means people like your product but hate how you’re presenting it. According to a 2026 eMarketer report, this kind of thematic analysis is becoming standard for figuring out specific campaign strengths instead of just relying on general feedback.

Pro Tip: Export all the negative mentions for a specific theme and read them yourself. This qualitative check can uncover subtle issues that automated tools might miss, like sarcasm or cultural references that went wrong.

What you’ll get is a detailed breakdown of what parts of your ads are driving positive or negative feelings, giving you clear, actionable ideas for how to optimize your creative.

Step 3: Integrating Post-Ad Survey Data for Deeper Perception Insights

Social listening gives you the raw, organic feedback, but direct surveys provide structured answers to your specific questions. Using both gives you the complete picture of ad sentiment.

3.1 Design and Deploy Post-Exposure Surveys

Use a tool like SurveyMonkey or Qualtrics to build some targeted surveys. You want to send these to people who’ve actually seen your ads, which you can do with retargeting segments, email lists, or panel surveys.

1. Ad Recall Questions: Start by confirming they saw the ad. “Do you recall seeing an advertisement for [Your Brand] in the past [timeframe]?”
2. Emotional Response Questions: Ask open-ended questions about feelings. “What emotions did the ad evoke?” or “Describe your overall feeling after watching/seeing the ad.” For numbers, use Likert scales: “The ad made me feel [happy, inspired, annoyed] (1-5 scale).”
3. Brand Association Questions: Connect the ad to brand perception. “After seeing the ad, what three words come to mind when you think of [Your Brand]?” or “Did the ad change your perception of [Your Brand]? If so, how?”
4. Call-to-Action Effectiveness: Ask about intent. “How likely are you to visit our website/make a purchase after seeing this ad?”

Pro Tip: Keep surveys short, maybe 5 to 7 questions max, to keep completion rates from tanking. If you can, offer a small incentive, like entry into a prize draw, to get more people to respond.

You’ll end up with hard numbers and qualitative feedback directly from your target audience about how they thought and felt about your ad.

3.2 Analyze Survey Data for Sentiment and Perception

Once you have enough responses, it’s time to dig in for sentiment and perception insights.

1. Sentiment Scoring from Open-Ended Responses: For those open-ended questions, you can either read through them manually or use a text analytics feature to assign sentiment. Look for phrases that keep popping up, both good and bad.
2. Correlation with Ad Metrics: Now, compare the survey responses to your ad platform data. Did the ads that scored high on positive sentiment in the surveys also have better conversion rates? This is how you prove the link between good vibes and good performance. A recent IAB report confirms that mixing sentiment metrics with traditional KPIs gives a much better sense of ROI.
3. Identify Perception Gaps: Was there a disconnect between the message you *thought* you were sending and what the audience actually heard? For example, if you were going for “innovation” but people mostly said the ad felt “playful,” you’ve got a problem to fix in the next round of creative.

Common Mistake: Analyzing survey data by itself. Its real value is when you combine it with social listening and your standard ad performance metrics. Otherwise, you’re just looking at disconnected pieces of a puzzle. Integration is what brings the whole picture into focus.

The result is a clear picture of how your ads are shaping brand perception and triggering specific emotions, which will directly inform your future creative and targeting.

Step 4: Actioning Insights and Optimizing Campaigns

Collecting data is pointless if you don’t use it to refine your advertising. This is where you actually apply what you’ve learned.

4.1 Iterative Creative Testing Based on Sentiment

Use what you’ve learned from social listening and surveys to guide your creative process. If one ad keeps getting negative comments about its tone, but people seem to like the product, you know the presentation is the problem, not the product. I’ve seen campaigns completely turn around just by changing the background music or voiceover based on this kind of specific feedback.

1. A/B Testing: Create new ad variations that address the sentiment issues you found. If the humor in an ad was seen as offensive, test a more straightforward version. Run these as A/B tests and watch both your standard KPIs and your sentiment scores in Brandwatch.
2. Targeting Adjustments: If a specific audience segment is consistently reacting negatively, you should probably refine your targeting to exclude them or create a different ad that speaks their language.
3. Message Refinement: If you see recurring themes in the negative comments (like “too expensive” or “I don’t get it”), then you need to tweak your ad copy to handle those objections or better explain your value.

Pro Tip: Don’t be afraid to pull an ad if the sentiment is overwhelmingly negative. The brand damage can easily outweigh any reach you might be getting. You have to be agile.

Do this right, and you’ll have optimized campaigns that create more positive sentiment, build a stronger brand, and in the end drive better business results.

4.2 Long-Term Brand Health Monitoring

Post-ad sentiment analysis is an ongoing process for monitoring long-term brand health. You can’t just do it once.

1. Baseline Establishment: You need to know your brand’s baseline sentiment *before* you launch a big campaign. This is the only way to accurately measure the incremental impact of your advertising.
2. Trend Analysis: Look for long-term trends. Is your brand’s overall positive sentiment growing? How does that line up with your ad spend and messaging over time? Is there a connection? (There should be).
3. Competitive Benchmarking: Regularly check your brand’s sentiment against your main competitors. Are your ads helping you win a more favorable perception in the market? A 2026 Nielsen report found that brands actively monitoring and adapting to sentiment see 15% higher brand equity growth than ones that don’t.

This gives you a continuous read on your brand’s position in the market, letting you make proactive changes to your marketing strategy to maintain a positive perception.

In a crowded digital ad space, looking past basic clicks to understand ad sentiment and customer perception is how you get an edge. When you systematically use social listening and surveys, you’re no longer just counting clicks, you’re understanding the emotional impact of your campaigns. That’s what leads to more effective advertising in 2026 and beyond. To improve your overall game, you might want to look at fixing your 2026 ad content strategy.