> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dolva.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Emotion Analysis: Understanding Dolva Affective Signals

> Emotion analysis surfaces affective states and valence from audio. Learn how Dolva detects emotional signals from voice recordings.

Emotion analysis is Dolva's second core analysis type. Sending an audio recording to the `/v1/analyze/emotion` endpoint returns a JSON payload describing the emotional signals present in the audio — including detected emotional states and valence (positive, negative, or neutral tone).

## What It Measures

Dolva's emotion models analyze the acoustic features of speech — such as pitch, energy, rhythm, and spectral characteristics — that are known to correlate with emotional expression. The models detect:

* **Emotional states** — e.g., calm, tense, engaged, distressed
* **Valence** — the positive or negative quality of the emotional tone
* **Signal confidence** — how strongly the detected emotion is expressed in the audio

All analysis is acoustic: Dolva processes the audio waveform directly and does not require transcription or language-specific models.

## Common Use Cases

* **Customer experience** — Detect emotional shifts in support calls to flag at-risk conversations
* **Wellbeing monitoring** — Track emotional patterns over time for individuals in therapeutic or coaching contexts
* **Content moderation** — Identify distress or agitation signals in voice-based platforms
* **Research and analytics** — Analyze emotional trends across large audio datasets

## How to Request Emotion Analysis

Send a `multipart/form-data` POST request to `/v1/analyze/emotion` with your audio file:

```bash curl theme={null}
curl -X POST https://api.dolva.ai/v1/analyze/emotion \
  -H "Authorization: Bearer dv-xxxxxxxx" \
  -F "audio=@conversation.wav"
```

The response is a JSON object containing the detected emotional signals.

## Relationship to Cognitive Analysis

Emotion and cognitive analyses are complementary. Running both on the same recording gives you a fuller picture: cognitive signals describe how the speaker is processing information, while emotion signals describe how they are feeling. See [Cognitive Analysis](/concepts/cognitive-analysis) to learn more.

<Card title="View the API Reference" icon="code" href="/api-reference/analyze-emotion">
  Full request/response details for POST /v1/analyze/emotion
</Card>