About Sentiment Analysis
Sentiment Analysis in Discorra shows you the emotional polarity of corpora — how much of the language is positive, neutral, or negative.
It helps identify not just what people are talking about, but the tone they use to express it.
Quick Links
- What is sentiment analysis?
- How sentiment is measured
- How to interpret distributions
- Comparative sentiment scoring
- Why sentiment matters
- Next steps
What is sentiment analysis?
Sentiment analysis classifies each sentence or token into one of three categories:
- Positive — words that express approval, optimism, or favorable tone
- Neutral — descriptive or factual language without emotional valence
- Negative — words that express criticism, dissatisfaction, or unfavorable tone
Definition
Sentiment analysis in Discorra measures the distribution of polarity values across a corpus and compares them across datasets.
How sentiment is measured
Discorra uses a lexicon-driven approach combined with weighting:
-
Polarity assignment
Tokens are mapped to positive, negative, or neutral sentiment scores. -
Distribution calculation
Each corpus is broken down into percentage shares (e.g., 25% positive, 68% neutral, 7% negative). -
Comparative scoring
The difference between corpora is displayed as Δ values for quick benchmarking.
How to interpret distributions
- Positive %: The proportion of optimistic or affirming language.
- Neutral %: The largest share in most corpora, representing factual or descriptive text.
- Negative %: Indicates criticism, risk, or emotional tension.
⚠️ Note: High resonance does not mean similar sentiment. Two corpora may use the same vocabulary but with very different tones.
Comparative sentiment scoring
Discorra highlights differences with two measures:
- Δ Positivity: How much more positive one corpus is compared to the other.
- Δ Comparative: A normalized score (−1 to +1) showing relative tone.
This makes it easy to benchmark brand voice vs. audience tone, or compare competitors directly.
Why sentiment matters
Sentiment analysis helps you:
- Benchmark brand vs. audience tone
- Detect emerging risks when negative sentiment increases
- Identify opportunity framing (e.g., shifting neutral terms into positive space)
- Measure campaign effectiveness in shaping perception
Example:
In a Jazz vs. Blues comparison, Jazz shows 25% positive vs. 28% for Blues. Though close, Blues leans slightly more positive. This signals a subtle difference in tone that may influence audience perception.