Customers Say a Lot. Pivony Understands What They Mean.
Standard sentiment = positive/negative. Pivony understands frustration, urgency, delight, confusion — across every channel, every segment, in real time.
Why Standard Sentiment Misleads You
Binary labels miss the nuance
"Positive" can mean satisfied, relieved, or passively accepting. "Negative" can mean irritated or about to churn. You need to know which.
No language handles Turkish
Generic NLP models are built on English data. Turkish morphology and idiomatic expression require dedicated NLU — and most platforms simply cannot do it.
Sentiment without context is useless
Knowing 30% of feedback is negative this week tells you nothing. Knowing frustrated VIP customers are talking about billing errors tells you everything.
Sentiment Analysis That Goes 10 Levels Deep
01
Ingest every feedback channel
Surveys, support tickets, chat logs, reviews, social mentions — all unified into one analysis stream.
02
Multi-dimensional emotion tagging
Pivony NLP tags frustration, urgency, delight, confusion, and sarcasm — not just positive or negative.
03
Segment by context
Sentiment by customer tier, product area, geography, channel — so you know exactly who feels what and about which part of your experience.
04
Act on emotional signals
Urgency spikes trigger alerts. Delight clusters surface promoter opportunities. Frustration patterns drive recovery workflows.
What You Can Measure
Emotion taxonomy
Frustration, urgency, delight, confusion, sarcasm — beyond positive/negative
Sentiment by segment
VIP vs standard, region vs region, channel vs channel
Topic-level sentiment
Customers love your delivery speed but hate your billing — known separately
Urgency detection
Flag feedback requiring immediate action before it escalates
Sentiment trend over time
Track emotional trajectory by segment week over week
Native Turkish NLU
Purpose-built Turkish language understanding — not translated English models
What to Expect from a Sentiment Analysis Platform
Common Questions
What makes Pivony sentiment analysis different from basic tools?＋
Most tools classify text as positive, negative, or neutral. Pivony identifies specific emotions — frustration, urgency, delight, confusion — and links them to customer segments, product areas, and channels. The result is actionable insight, not just a score.
Why is native Turkish NLU important?＋
Turkish is an agglutinative language — words are built by stacking suffixes, and meaning changes dramatically with each. Generic NLP models trained on English perform poorly on Turkish. Pivony was built from the ground up with Turkish NLU as a core capability, making it far more accurate for Turkish-speaking markets.
Can Pivony detect sarcasm and irony?＋
Yes. Pivony NLP is trained to identify sarcastic and ironic expressions, which are notoriously misclassified by standard positive/negative models — especially in Turkish.
How is urgency detection different from sentiment scoring?＋
Urgency detection identifies when a customer is at immediate risk of churning or escalating — regardless of overall sentiment score. A customer might use measured language but express clear intent to leave. Urgony flags that signal.
Which channels does Pivony analyze for sentiment?＋
NPS surveys, CSAT responses, support tickets, call centre transcripts, app reviews, live chat, and social mentions. All channels are analyzed with the same NLP model so results are directly comparable.
Ready to understand what your customers are actually feeling?
Request a demo and see emotion-level sentiment analysis on your own feedback data.