Every call your team handles tells a story about how your customers feel. dialnote automatically analyzes the sentiment of every call — both AI-handled and human-handled — so you can spot trends, catch problems early, and coach your team without listening to hours of recordings.

How It Works#

dialnote assigns one of three sentiment values to each call:

• Positive 😊 — The caller was happy, satisfied, or had a good experience
• Neutral 😐 — The conversation was matter-of-fact, neither positive nor negative
• Negative 😟 — The caller was frustrated, upset, or had a bad experience

The way sentiment gets analyzed depends on the call type:

AI voice agent calls are analyzed by the AI engine (Retell) that powers your agents. After each call wraps up, Retell evaluates the full conversation and sends back a sentiment score automatically. There's nothing to configure — it just works.

Human-handled calls go through dialnote's built-in LLM evaluation. Once a call's transcript is ready, the system runs it through a language model that reads the entire conversation and determines the caller's overall sentiment. This runs alongside call scoring, so you get both a numeric score (1–10) and a sentiment label from the same analysis.

Viewing Sentiment Data#

You'll find sentiment data in a few places across dialnote:

Analytics Dashboard#

Head to Analytics → AI Agents to see the Sentiment Analysis chart. It shows a horizontal bar breaking down all calls by percentage — green for positive, gray for neutral, and red for negative. This gives you a quick read on overall customer satisfaction across your AI agents.

The AI Insights section on the same page uses sentiment data to generate actionable recommendations. If more than 20% of calls show negative sentiment, dialnote flags it as an opportunity and suggests reviewing common pain points. When positive sentiment exceeds 60%, you'll see a success indicator confirming strong customer satisfaction.

Individual Call Records#

Each call record in the AI calls table includes a sentiment indicator. You can filter the table by sentiment to quickly find all negative calls that might need follow-up, or all positive ones you can use as training examples.

Using Sentiment to Improve#

Sentiment data is most useful when you track it over time and act on patterns:

For AI agents: If you notice a spike in negative sentiment, check the transcripts of those calls. Common causes include missing information in your knowledge base, confusing agent instructions, or scenarios your agent isn't set up to handle. Update your agent's instructions or add a new tool to address the gap.

For your team: Negative sentiment trends on specific phone numbers or with specific team members can point to training opportunities. Pair this with call scores to get the full picture — a call might score well on process (high score) but still leave the customer frustrated (negative sentiment).

For reporting: Export your agent performance data as CSV from the analytics page to share sentiment trends with your team. The cost savings metric also ties into sentiment indirectly — calls that are fully contained by AI with positive sentiment are your highest-value automation wins.

Sentiment and Call Scoring#

Sentiment and call scoring work together but measure different things. Call scoring rates how well the call was handled on a 1–10 scale — did the agent follow the right steps, resolve the issue, stay professional? Sentiment captures how the caller felt about the interaction.

It's possible to have a high-scoring call with negative sentiment (the agent did everything right, but the customer was already upset about a billing issue). It's also possible to have a low-scoring call with positive sentiment (the agent skipped steps, but the customer was easy-going and got what they needed).

Both metrics are generated from the same transcript analysis for human calls, so there's no extra setup required. Check the Call Scoring docs for more details on how numeric scores work.