Why Brands Should Shift from Sentiment Analysis to Favorability

When tracking online mentions, sentiment analysis has long been a staple for quantifying public perception. However, while sentiment analysis uses natural language processing (NLP) to categorize language as positive, negative, or neutral, it often struggles to understand context. This can lead to unreliable and even misleading results, especially in nuanced scenarios where tone and intent diverge. For organizations aiming to protect their reputation and act on emerging trends, it’s time to move beyond sentiment analysis and adopt a more precise approach: favorability.

The Limits of Sentiment Analysis

Sentiment analysis relies heavily on the surface-level emotional tone of a statement, often missing the context and deeper meaning behind the words. Here are some key challenges:

Context Matters
Sentiment analysis tools tend to misinterpret statements when the emotional tone doesn’t align with the context. For example, during the UnitedHealthcare CEO shooter incident, some online comments referred to the shooter as “rich.” At first glance, sentiment analysis might classify this as positive because “rich” often carries a favorable connotation. However, the term was being used sarcastically to criticize the shooter’s perceived privilege and contrast it with struggles faced by others. This crucial nuance is lost in traditional sentiment models.
Literal Bias
A classic example is in sports. A statement like “The Dolphins killed the Bills” would likely be categorized as negative because of the word “killed.” But in the context of sports, it’s a favorable statement for the Dolphins, as it refers to their victory. Sentiment analysis often fails to recognize such domain-specific language.
Neutral Classifications Hide Insights
Ambiguous or complex statements are frequently categorized as neutral, which limits their utility. This default to neutrality often misses key nuances, leading to underwhelming insights.
Tone ≠ Intent
Positive-sounding language can carry criticism, and negative language can sometimes highlight strengths. For example, sarcastic praise or backhanded compliments are rarely detected accurately by sentiment analysis.
High Mislabeling Rates
Studies and industry findings suggest that traditional sentiment analysis tools can misclassify up to 35% of data due to challenges like sarcasm, mixed sentiment, negations, and lack of contextual understanding. For businesses relying on these tools, this margin of error can lead to flawed insights, missed opportunities, or misdirected responses.

What Is Favorability and How Is It Calculated?

Favorability, on the other hand, is designed to more accurately assess the context, intent, and perspective of a statement, addressing the shortcomings of traditional sentiment analysis. While sentiment analysis classifies language based solely on emotional tone—positive, negative, or neutral—favorability factors in the overall context, outcome, and domain-specific nuances.

For example, the statement “The Dolphins killed the Bills” might be flagged as negative in traditional sentiment models because of the word “killed.” However, favorability identifies the statement as favorable for the Dolphins (a victory) and unfavorable for the Bills (a loss). By assessing the perspective and intended meaning rather than tone alone, favorability delivers nuanced, actionable insights.

Favorability is calculated using a custom machine-learning model tailored to your organization’s needs. Here’s how it works:

Training the Model: PeakMetrics analysts manually tag mentions around a brand or topic as favorable, unfavorable, or neutral. Importantly, these tags are applied from a specific perspective—whether it’s your brand, a competitor, or another entity.
Continuous Improvement: The PeakMetrics data science team uses this tagged data to train a classifier that can detect patterns and evaluate favorability from the correct perspective. As new topics emerge, models can be retrained to maintain accuracy.
Automated Enrichment: Once deployed, mentions are automatically enriched with favorability tags, accurately identifying whether the content is favorable, unfavorable, or neutral toward the defined entity.

Within the PeakMetrics platform, favorability integrates seamlessly into your workflow. Users can filter mentions by favorability, focus on unfavorable perspectives for threat detection, or surface favorable narratives to amplify positive opportunities. By anchoring the analysis to the appropriate perspective, PeakMetrics ensures organizations gain a clear, context-driven understanding of how online narratives impact their brand, industry, and reputation.

How PeakMetrics Leverages Favorability

Favorability isn’t just a standalone tool at PeakMetrics—it’s a cornerstone of our narrative intelligence platform. We use favorability to enrich our threat scoring system, ensuring that every narrative is accurately assessed for its potential impact. Whether identifying opportunities to amplify positive stories or mitigating unfavorable narratives before they escalate, favorability plays a critical role in helping organizations stay ahead of emerging trends and threats.

Our approach is collaborative: we work with you to customize the favorability model to fit your specific needs, using data to train a classifier that understands your organization’s unique context. In early studies with a customer, we were able to achieve 90% accuracy.

Right now, we’re seeing favorability significantly enhance insights across industries, from highlighting geo-political risks to identifying early warning signs of brand-related crises. This enhanced precision makes it a powerful tool for tracking how online narratives evolve and assessing their true significance.

Why Favorability Matters

Switching from sentiment analysis to favorability isn’t just an upgrade—it’s a necessity for brands navigating complex information environments. Here’s why:

Improved Narrative Understanding: Favorability captures the intent and impact of language, giving you a more accurate picture of public perception.
Proactive Threat Management: Identify unfavorable narratives early, before they escalate into crises.
Data You Can Act On: Favorability equips teams with clear, actionable insights rather than ambiguous classifications.
Strategic Decision-Making: Use precise intelligence to inform responses and shape strategies confidently.

For example, favorability ensures that a phrase like “rich” is correctly identified as unfavorable in the UnitedHealthcare CEO shooter narrative, while also recognizing sports jargon like “killed” as favorable when describing a team’s victory. This level of contextual understanding is critical for brands operating in high-stakes industries.

The Future Is Favorability

Sentiment analysis has served its purpose, but it’s no longer enough to meet the demands of today’s complex narrative landscape. Favorability provides a deeper, more accurate understanding of public perception, empowering organizations to navigate online narratives with clarity and confidence.