While sentiment analysis categorizes mentions as positive, negative, or neutral, emotion analysis provides a more detailed and human-level understanding of your audience’s tone.
BrandMentions’ Emotion Analysis feature goes beyond sentiment scores to detect the specific emotions expressed in mentions such as Love, Joy, Surprise, Anger, Sadness, Fear, and Disgust.
By analyzing these emotions, you can uncover the why behind audience sentiment, gain deeper empathy for your customers, and align your communication, marketing, and product strategies accordingly.
Accessing Emotion Analysis in the Analytics Dashboard
The main tool for analyzing emotions is the Emotion Over Time chart, located in the Analytics Dashboard.
1. Navigate to the Analytics Dashboard
From the main BrandMentions menu, go to Analytics → Emotion Analysis.
2. Locate the “Emotion Over Time” Chart
You’ll find a multi-line chart visualizing emotional trends over your selected time range. Each emotion such as Joy, Anger, or Sadness is represented by a distinct color, allowing you to track shifts over time.
3. Interpret the Chart
By observing these patterns, you can identify which emotions dominate your brand’s online conversations and how they evolve.
For example:
A spike in Joy and Love may indicate a successful marketing campaign or product launch.
A rise in Anger or Disgust could signal customer dissatisfaction or a product/service issue.
An increase in Sadness might relate to disappointment, such as the discontinuation of a popular product.
Tip: Use filters and custom date ranges to isolate events or campaigns and see how they influenced audience emotion.
Practical Applications of Emotion Analysis
Emotion Analysis offers actionable insights across multiple areas of your business.
1. Refine Marketing and Messaging
Use emotion data to tailor your brand voice and campaign tone.
If your brand evokes Joy and Love, highlight these emotions in your visuals and messaging.
If you detect Fear or Sadness linked to specific pain points, adjust your communication to offer reassurance, solutions, and empathy.
This ensures your campaigns connect emotionally and authentically with your audience.
2. Improve Customer Service
Understanding emotional context enables your support team to respond with empathy and precision.
A customer expressing Anger may need acknowledgment and quick resolution.
One showing Sadness might require reassurance or follow-up support.
Emotion analysis helps personalize interactions and enhance customer satisfaction.
3. Gain Deeper Product Insights
Emotion data reveals how users feel about your products and features — beyond what they explicitly say.
A surge in Surprise could indicate confusion or unexpected functionality.
Repeated mentions tied to Anger suggest a usability issue or unmet expectation.
By identifying emotional reactions early, you can prioritize improvements that matter most to users.
4. Humanize Your Brand
Responding with emotional awareness strengthens relationships and builds trust.
When you acknowledge customer emotions — whether celebrating Joy or addressing Frustration — you show that your brand listens, cares, and acts with empathy.
This emotional intelligence helps transform audiences into loyal advocates.
Combining Emotion Data with Other Metrics
For even deeper insights, correlate emotion analysis with other BrandMentions data sources, such as:
Influencer Mentions: See what emotions your most influential voices evoke.
Reach Metrics: Identify which emotions drive the highest visibility and engagement.
Sentiment Trends: Combine emotion and sentiment data for a full emotional map of your brand’s perception.
Example: If high-reach mentions consistently express Anger, the issue may be spreading faster and deserves immediate attention.
Best Practices for Emotion Analysis
Monitor regularly: Review emotional trends weekly or monthly to stay attuned to your audience’s tone.
Contextualize with campaigns: Always connect emotion spikes to specific events, launches, or announcements.
Act on patterns: Don’t just observe emotions — translate them into product, service, and messaging improvements.
Use real-time alerts (if applicable): Combine emotion monitoring with sentiment alerts for faster detection of tone shifts.