> ## Documentation Index
> Fetch the complete documentation index at: https://assembly-preview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Sentiment Analysis

> The Sentiment Analysis model detects the sentiment of each spoken sentence in the transcript text. Use Sentiment Analysis to get a detailed analysis of the positive, negative, or neutral sentiment conveyed in the audio, along with a confidence score for each result.

## Quickstart

Enable Sentiment Analysis by setting `sentiment_analysis` to `true` in the transcription config.

<CodeGroup>
  ```python Python theme={"system"}
  import assemblyai as aai

  aai.settings.api_key = "YOUR_API_KEY"

  # audio_file = "./local_file.mp3"
  audio_file = "https://assembly.ai/wildfires.mp3"

  config = aai.TranscriptionConfig(sentiment_analysis=True)

  transcript = aai.Transcriber().transcribe(audio_file, config)

  for sentiment_result in transcript.sentiment_analysis:
      print(sentiment_result.text)
      print(sentiment_result.sentiment)  # POSITIVE, NEUTRAL, or NEGATIVE
      print(sentiment_result.confidence)
      print(f"Timestamp: {sentiment_result.start} - {sentiment_result.end}")
  ```

  ```typescript TypeScript theme={"system"}
  import { AssemblyAI } from 'assemblyai'

  const client = new AssemblyAI({
    apiKey: 'YOUR_API_KEY'
  })

  // const audioFile = './local_file.mp3'
  const audioFile =
    'https://assembly.ai/wildfires.mp3'

  const params = {
    audio: audioFile,
    sentiment_analysis: true
  }

  const run = async () => {
    const transcript = await client.transcripts.transcribe(params)

    for (const result of transcript.sentiment_analysis_results!) {
      console.log(result.text)
      console.log(result.sentiment)
      console.log(result.confidence)
      console.log(`Timestamp: ${result.start} - ${result.end}`)
    }
  }

  run()
  ```

  ```go Go theme={"system"}
  package main

  import (
      "context"
      "fmt"

      aai "github.com/AssemblyAI/assemblyai-go-sdk"
  )

  func main() {
      client := aai.NewClient("YOUR_API_KEY")

      // For local files see our Getting Started guides.
      audioURL := "https://assembly.ai/wildfires.mp3"

      ctx := context.Background()

      transcript, _ := client.Transcripts.TranscribeFromURL(ctx, audioURL, &aai.TranscriptOptionalParams{
          SentimentAnalysis: aai.Bool(true),
      })

      for _, result := range transcript.SentimentAnalysisResults {
          fmt.Println(aai.ToString(result.Text))
          fmt.Println(result.Sentiment)
          fmt.Println(aai.ToFloat64(result.Confidence))
          fmt.Println("Timestamp:",
              aai.ToInt64(result.Start), "-",
              aai.ToInt64(result.End),
          )
      }
  }
  ```

  ```java Java theme={"system"}
  import com.assemblyai.api.AssemblyAI;
  import com.assemblyai.api.resources.transcripts.types.*;

  public final class App {
      public static void main(String[] args) {

          AssemblyAI client = AssemblyAI.builder()
                  .apiKey("YOUR_API_KEY")
                  .build();

          // For local files see our Getting Started guides.
          String audioUrl = "https://assembly.ai/wildfires.mp3";

          var params = TranscriptOptionalParams.builder()
                  .sentimentAnalysis(true)
                  .build();

          Transcript transcript = client.transcripts().transcribe(audioUrl, params);

          var sentimentAnalysisResults = transcript.getSentimentAnalysisResults().get();

          sentimentAnalysisResults.forEach(result -> {
              System.out.println(result.getText());
              System.out.println(result.getSentiment()); // POSITIVE, NEUTRAL, or NEGATIVE
              System.out.println(result.getConfidence());
              System.out.println("Timestamp: " + result.getStart() + " - " + result.getEnd());
          });
      }
  }
  ```

  ```C# C# theme={"system"}
  using AssemblyAI;
  using AssemblyAI.Transcripts;

  var client = new AssemblyAIClient("YOUR_API_KEY");

  var transcript = await client.Transcripts.TranscribeAsync(new TranscriptParams
  {
      // For local files see our Getting Started guides.
      AudioUrl = "https://assembly.ai/wildfires.mp3",
      SentimentAnalysis = true
  });

  foreach (var result in transcript.SentimentAnalysisResults!)
  {
      Console.WriteLine(result.Text);
      Console.WriteLine(result.Sentiment); // POSITIVE, NEUTRAL, or NEGATIVE
      Console.WriteLine(result.Confidence);
      Console.WriteLine($"Timestamp: {result.Start} - {result.End}");
  }
  ```

  ```Ruby Ruby theme={"system"}
  require 'assemblyai'

  client = AssemblyAI::Client.new(api_key: 'YOUR_API_KEY')

  # For local files see our Getting Started guides.
  audio_url = 'https://assembly.ai/wildfires.mp3'

  transcript = client.transcripts.transcribe(
    audio_url: audio_url,
    sentiment_analysis: true
  )

  transcript.sentiment_analysis_results.each do |result|
    puts result.text
    puts result.sentiment
    puts result.confidence
    printf("%<start>d - %<end>d\n", start: result.start, end: result.end_)
  end
  ```
</CodeGroup>

### Example output

```bash theme={"system"}
Smoke from hundreds of wildfires in Canada is triggering air quality alerts throughout the US.
SentimentType.negative
0.8181032538414001
Timestamp: 250 - 6350
...
```

## Add speaker labels to sentiments

To add speaker labels to each sentiment analysis result, using [Speaker Diarization](/docs/speech-to-text/speaker-diarization), enable `speaker_labels` in the transcription config.

Each sentiment result will then have a `speaker` field that contains the speaker label.

<CodeGroup>
  ```python Python theme={"system"}
  config = aai.TranscriptionConfig(
    sentiment_analysis=True,
    speaker_labels=True
  )

  # ...

  for sentiment_result in transcript.sentiment_analysis:
    print(sentiment_result.speaker)
  ```

  ```typescript TypeScript theme={"system"}
  const params = {
    audio: audioUrl,
    sentiment_analysis: true,
    speaker_labels: true
  }

  // ...

  for (const result of transcript.sentiment_analysis_results!) {
    console.log(result.speaker)
  }
  ```

  ```Go go theme={"system"}
  transcript, _ := client.Transcripts.TranscribeFromURL(ctx, audioURL, &aai.TranscriptOptionalParams{
      SentimentAnalysis: aai.Bool(true),
      SpeakerLabels:     aai.Bool(true),
  })

  for _, result := range transcript.SentimentAnalysisResults {
      fmt.Println(aai.ToString(result.Speaker))
  }
  ```

  ```java Java theme={"system"}
  var params = TranscriptOptionalParams.builder()
          .sentimentAnalysis(true)
          .speakerLabels(true)
          .build();

  // ...

  sentimentAnalysisResults.forEach(result -> {
      System.out.println(result.getSpeaker());
  });
  ```

  ```C# C# theme={"system"}
  var transcript = await client.Transcripts.TranscribeAsync(new TranscriptParams
  {
      AudioUrl = audioUrl,
      SentimentAnalysis = true,
      SpeakerLabels = true
  });

  // ...

  foreach (var result in transcript.SentimentAnalysisResults!)
  {
      // ...
      Console.WriteLine(result.Speaker);
  }
  ```

  ```Ruby Ruby theme={"system"}
  transcript = client.transcripts.transcribe(
    audio_url: audio_url,
    sentiment_analysis: true,
    speaker_labels: true
  )

  # ...

  transcript.sentiment_analysis_results.each do |result|
    puts result.speaker
  end
  ```
</CodeGroup>

## API reference

### Request

```bash theme={"system"}
curl https://api.assemblyai.com/v2/transcript \
--header "Authorization: YOUR_API_KEY" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL",
  "sentiment_analysis": true
}'
```

| Key                  | Type    | Description                |
| -------------------- | ------- | -------------------------- |
| `sentiment_analysis` | boolean | Enable Sentiment Analysis. |

### Response

```json theme={"system"}
{
  "sentiment_analysis_results": [...]
}
```

| Key                                        | Type           | Description                                                                                                                |
| ------------------------------------------ | -------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `sentiment_analysis_results`               | array          | A temporal sequence of Sentiment Analysis results for the audio file, one element for each sentence in the file.           |
| `sentiment_analysis_results[i].text`       | string         | The transcript of the i-th sentence.                                                                                       |
| `sentiment_analysis_results[i].start`      | number         | The starting time, in milliseconds, of the i-th sentence.                                                                  |
| `sentiment_analysis_results[i].end`        | number         | The ending time, in milliseconds, of the i-th sentence.                                                                    |
| `sentiment_analysis_results[i].sentiment`  | string         | The detected sentiment for the i-th sentence, one of `POSITIVE`, `NEUTRAL`, `NEGATIVE`.                                    |
| `sentiment_analysis_results[i].confidence` | number         | The confidence score for the detected sentiment of the i-th sentence, from 0 to 1.                                         |
| `sentiment_analysis_results[i].speaker`    | string or null | The speaker of the i-th sentence if [Speaker Diarization](/docs/speech-to-text/speaker-diarization) is enabled, else null. |

## Frequently asked questions

<AccordionGroup>
  <Accordion title="What if the model predicts the wrong sentiment label for a sentence?">
    The Sentiment Analysis model is based on the interpretation of the transcript and may not always accurately capture the intended sentiment of the speaker. It's recommended to take into account the context of the transcript and to validate the sentiment analysis results with human judgment when possible.
  </Accordion>

  <Accordion title="What if the transcript contains sensitive or offensive content?">
    The [Content Moderation model](/docs/audio-intelligence/content-moderation) can be used to identify and filter out sensitive or offensive content from the transcript.
  </Accordion>

  <Accordion title="What if the sentiment analysis results aren't consistent with my expectations?">
    It's important to ensure that the audio being analyzed is relevant to your use case. Additionally, it's recommended to take into account the context of the transcript and to evaluate the confidence score for each sentiment label.
  </Accordion>

  <Accordion title="What if the sentiment analysis is taking too long to process?">
    The Sentiment Analysis model is designed to be fast and efficient, but processing times may vary depending on the size of the audio file and the complexity of the language used. If you experience longer processing times than expected, don't hesitate to contact our support team.
  </Accordion>
</AccordionGroup>