---
schema_version: "1"
id: 447f537709fe0e56fc8b69557d635ae0664f870acc0bb15642e5a74909b1c005
title: Community Sentiment Model
collection: developer
version: current
locale: en-us
portal_url: https://docs.salesforce.com/developer/current/en-us/einstein-vision-language/einstein/guides/Use-Prebuilt-Language-Models/use-pre-built-models-sentiment.md
canonical_url: https://developer.salesforce.com/docs/analytics/einstein-vision-language/guide/use-pre-built-models-sentiment.html
source_site: developer.salesforce.com
content_type: documentation
content_hash: 4f5fd74a66545dd9028708c328b2bb22046b829654c5c186ab2d1c71d4bb3b84
products:
  - Einstein Vision and Language
  - Einstein Vision and Language Repo
---

# Community Sentiment Model

Einstein Language offers a pre-built sentiment model that you can use as long as you have a valid JWT token. This model has three classes:

- positive
- negative
- neutral

Use the community sentiment model to classify text without building your own custom model. This model was created from data that comes from multiple sources. The data is short snippets of text, about one or two sentences, and similar to what you would find in a public community or Chatter group, a review/feedback forum, or enterprise social media.

This cURL command sends in a text string and returns a prediction from the model. You call the pre-built model the same way you call a custom model, but instead of passing in your own `modelId`, you pass in a `modelId` of `CommunitySentiment`. See [Prediction for Sentiment](/docs/analytics/einstein-vision-language/references).

```cURL
curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "modelId=CommunitySentiment" -F "document=the presentation was great and I learned a lot"  https://api.einstein.ai/v2/language/sentiment
```

The model returns a result similar to the following JSON.

```json
{
  "probabilities": [
    {
      "probability": 0.9435403,
      "label": "positive"
    },
    {
      "probability": 0.031840604,
      "label": "negative"
    },
    {
      "probability": 0.02461909,
      "label": "neutral"
    }
  ],
  "object": "predictresponse"
}
```