Using AI to catch emotional shifts before crisis hits

Bianca Prade on how real-time sentiment can shape smarter, faster strategy.

AI helped me

Today, a brand’s reputation can move as fast as a consumer can send a tweet. A single post or campaign misstep can spark a wave of sentiment – positive or negative – that quickly gains traction and becomes difficult to reverse.

“Emotion moves fast,” said Bianca Prade, CEO of BStrategies and a visiting scholar at George Washington University. She and her team have developed an AI approach specifically to monitor sentiment to help differentiate between “noise” and something “meaningful.”

 

 

Prade noted that if communicators aren’t equipped to respond with equal speed, reputational damage can spread in minutes. She gave the recent example of Coca-Cola’s AI-generated holiday film, which sparked rapid sentiment shifts last November

“Social chatter flipped from praise to concern in roughly 70 minutes,” she recalled. “Artists and consumers called the visuals ‘soulless’ and worried about AI replacing human creatives. Net sentiment on X fell about 10 points per hour.”

The brand’s delayed response – four days for a media statement and a week for the brand’s  VP and global head of generative AI to comment – allowed the “creepy AI Christmas” narrative to take hold, Prade said.

Prade, who’ll lead the session titled “From Sentiment to Strategy” at The PR Daily Conference later this week , said it’s vital for PR teams to be able to “spot meaningful emotional shifts in real time and convert those signals into faster crisis response, smarter campaigns.”

“Catch emotion dips early or risk losing control of the story,” Prade said.

Best practices for AI in sentiment analysis

Rather than chasing viral spikes or trending hashtags, Prade’s team focuses on “velocity and intensity, not raw volume.”

“Five angry tweets from verified accounts in 15 minutes matter more than 500 neutral mentions all day,” she said.

To filter out false signals, they cluster emotion words to detect sarcasm or exaggerated sentiment. Once they’ve detected a sentiment shift, they map the top three emotions to a story frame.

“Anger often calls for a justice angle, joy calls for a celebration angle,” Prade explained. Using “traffic-light thresholds” – a simple, color-coded system – helps leaders know exactly when to pivot copy, shift timing or retarget audiences.

  • Green: Sentiment steady or rising, no action
  • Yellow: Small dip, watch and prep response
  • Red: Sharp drop, launch rapid-response playbook

This structured approach enables quick yet nuanced responses – critical as digital conversations fragment and emotional peaks accelerate, Prade said.

One example from a private community platform her firm manages: when an AI dashboard flagged a sudden rise in frustration after a new course price was announced, the team acted quickly.

Within an hour they paused promotions, opened an Ask-Me-Anything thread with the instructor and offered a short early-bird discount. “Positivity rebounded within 48 hours and course sign-ups returned to plan,” Prade said, largely because they caught a sentiment change before things spiraled out of control.

For teams starting out, Prade recommends a measured approach: run a small pilot. Feed one live campaign – perhaps a product launch or an executive post – into a basic sentiment-tracking tool and set a simple 10% velocity alert to flag sudden emotional shifts. Assign someone to monitor changes and document what triggered them.

After the campaign wraps, hold a team retrospective to review what happened, how the team responded and what impact it had.

“A practical win sells the budget better than a white paper,” Prade said.

Don’t lose sight of the human factor

While configuring an AI autopilot for a community platform, Prade has an “aha moment” about sentiment analysis. She noted that using it proactively can help safeguard against any unforced errors in terms of brand reputation.

In that case, the bot surfaced trending questions but also suggested threads that felt off-tone,” she said. Now Prade’s team has moderators who review the AI queue each morning, rephrase prompts in brand voice and add any needed nuance.

Building out digital twins – virtual replicas of target personas – using AI can help with that analysis by providing insights into how people might react. Uploading previous successful statements and example messages can help as well.

“Engagement improved once we blended the bot’s pattern recognition with human judgment,” she said. “The surprise was how quickly members spotted the difference.”

Even the most advanced artificial intelligence sentiment tool has limitations, Prade noted.

“It struggles with thin data sets, niche jargon and sarcastic memes in video or imagery,” she said. “Communicators should avoid declaring a trend on fewer than a few hundred coded mentions and always pair machine output with human context.” She also advised telling clients when AI is used to enhance accountability.

Looking ahead, Prade voiced excitement about multimodal sentiment analysis that blends voice and video cues with text. She’s more cautious about “black-box vendor models that hide training data.”

“Transparency and human QA must evolve alongside the tech,” she said.

The PR Daily Conference will take place May 21-23 in Washington, D.C.

Casey Weldon is a reporter for PR Daily. Follow him on LinkedIn.

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