Glossary
Stance Analysis
What do people actually support or oppose?
Definition
Stance analysis classifies whether a comment supports, opposes, or is neutral toward a specific claim or topic — independent of emotional tone. A commenter can use negative language while still supporting the position in question.
How it works
- 1
Each comment is read in context of the original post or claim.
- 2
The AI identifies the implied position: support, opposition, or neutrality.
- 3
Results are aggregated with engagement weighting so high-reach comments count more.
- 4
The distribution reveals whether public opinion actually leans for or against a claim.
Why it matters
PR teams and researchers who rely on sentiment alone routinely misread public opinion. A thread full of sarcastic negative comments can still reflect strong support for a brand. Stance analysis surfaces the real opinion structure that sentiment tools miss.
Related distinctions
Stance analysis vs sentiment analysis
Sentiment measures emotional valence (positive/negative/neutral). Stance measures alignment with a position (support/oppose/neutral). A comment like "I can't believe how well this product works" scores negative-ish on sentiment but is clearly supportive. Stance analysis is the more informative signal for opinion research.
Stance analysis vs opinion mining
Opinion mining is a broad category covering all techniques that extract opinions from text. Stance analysis is a specific method within opinion mining focused on directional alignment rather than just sentiment or topic extraction.
Frequently asked questions
What is stance analysis in social media?
Stance analysis in social media classifies whether a comment supports, opposes, or is neutral toward the subject of a post — based on the position expressed, not the emotional tone of the language.
How is stance analysis different from sentiment analysis?
Sentiment analysis measures emotional tone (positive, negative, neutral). Stance analysis measures directional alignment with a specific claim. The same comment can score negative in sentiment while being clearly supportive in stance.
Why do agencies use stance analysis?
Agencies use stance analysis to understand what proportion of a comment section actually opposes or supports a claim — important for crisis communications, campaign evaluation, and public affairs research where emotional tone alone is not enough.
Can stance analysis detect sarcasm?
Modern AI-powered stance models factor in sarcasm likelihood as part of the classification. A comment that reads as negative in tone but is clearly sarcastic in context — for example, "Oh sure, because that always works" — can still be classified as opposition rather than neutral.
See stance analysis in practice
Narativ applies stance analysis, narrative clustering, and engagement weighting to live comment sections — from £1 per post.