Skip to main content
The sentiment and emotion analysis model analyzes the transcript, detecting the general sentiment which is conveyed in each sentence (positive, neutral or negative) as well as any emotion that is being expressed.

​
Usage

To enable sentiment and emotion analysis simply set the appropriate flag: 
{
  "realtime_processing": {
    "sentiment_analysis": true
  },
  "messages_config": {
    "receive_realtime_processing_events": true
  }
}

​
Result

Your transcription result will contain a sentiment_analysis key which will contain an array of all sentences in the audio, and for each one the conveyed sentiment and expressed emotion. See possible values below.
When diarization is enabled, the sentiment analysis output will contain the speaker as well, allowing you to analyze each speaker separately.
result
{
  "transcription":{...},
  "sentiment_analysis": {
    "success": true,
    "is_empty": false,
    "results": [
      {
        "text": "Jonathan, it says you are trained in technology.",
        "sentiment": "neutral",
        "emotion": "neutral",
        "start": 0.45158000000000004,
        "end": 2.364,
        "channel": 0,
        "speaker": 0,
      },
      {
        "text": "That's very good.",
        "sentiment": "positive",
        "emotion": "positive_surprise",
        "start": 2.54438,
        "end": 3.5432300000000003,
        "channel": 0,
        "speaker": 0,
      }
    ],
    "exec_time": 1.127103805541992,
    "error": null
  }
}

​
Possible values

​
Sentiments

  • positive
  • negative
  • neutral
  • mixed
  • unknown

​
Emotions

  • adoration
  • amusement
  • anger
  • awe
  • confusion
  • contempt
  • contentment
  • desire
  • disappointment
  • disgust
  • distress
  • ecstatic
  • elation
  • embarrassment
  • fear
  • interest
  • pain
  • realization
  • relief
  • sadness
  • negative_surprise
  • positive_surprise
  • sympathy
  • triumph
  • neutral
⌘I