---
title: "AI Endpoints - Create a sentiment analyzer"
url: https://docs.ovhcloud.com/it/guides/public-cloud/ai-machine-learning/ai-endpoints-sentiment-analyzer
lang: it
lastUpdated: 2025-07-31
---
# AI Endpoints - Create a sentiment analyzer

:::info
AI Endpoints is covered by the **[OVHcloud AI Endpoints Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/48743bf-AI_Endpoints-ALL-1.1.pdf)** and the **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**.
:::

## Introduction

In this tutorial, we'll show you how to create a sentiment analyzer using **[AI Endpoints](https://endpoints.ai.cloud.ovh.net/)** and Java with **[Quarkus](https://github.com/quarkusio/quarkus)**.

We'll use a model from the `Natural Language Processing (NLP)` category, specifically the `roberta-base-go_emotions` model. This model can analyze text and return emotions in response.

### Project setup

To simplify the project, we'll use **[Quarkus](https://github.com/quarkusio/quarkus)** for fast development and REST exposure.

Start by adding the necessary dependencies in your `pom.xml`:

```xml
<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.ovhcloud.examples.aiendpoints</groupId>
  <artifactId>nlp</artifactId>
  <version>1.0.0-SNAPSHOT</version>

  <name>nlp</name>
  <url>https://endpoints.ai.cloud.ovh.net/</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <compiler-plugin.version>3.12.1</compiler-plugin.version>
    <maven.compiler.source>21</maven.compiler.source>
    <maven.compiler.target>21</maven.compiler.target>
    <maven.compiler.release>21</maven.compiler.release>
    <quarkus.platform.artifact-id>quarkus-bom</quarkus.platform.artifact-id>
    <quarkus.platform.group-id>io.quarkus.platform</quarkus.platform.group-id>
    <quarkus.platform.version>3.11.0</quarkus.platform.version>
    <skipITs>true</skipITs>
    <surefire-plugin.version>3.2.5</surefire-plugin.version>
  </properties>

  <dependencyManagement>
    <dependencies>
      <dependency>
        <groupId>${quarkus.platform.group-id}</groupId>
        <artifactId>${quarkus.platform.artifact-id}</artifactId>
        <version>${quarkus.platform.version}</version>
        <type>pom</type>
        <scope>import</scope>
      </dependency>
    </dependencies>
  </dependencyManagement>

  <dependencies>
    <dependency>
      <groupId>io.quarkus</groupId>
      <artifactId>quarkus-arc</artifactId>
    </dependency>
    <dependency>
      <groupId>io.quarkus</groupId>
      <artifactId>quarkus-rest</artifactId>
    </dependency>
    <dependency>
      <groupId>io.quarkus</groupId>
      <artifactId>quarkus-rest-client-jackson</artifactId>
  </dependency>
    <dependency>
      <groupId>io.quarkus</groupId>
      <artifactId>quarkus-junit5</artifactId>
      <scope>test</scope>
    </dependency>
  </dependencies>

  

</project>
```

_A full pom.xml example is available [here](https://github.com/ovh/public-cloud-examples/blob/main/ai/ai-endpoints/java-nlp/pom.xml)._

### Define the AI Endpoints client

We define a REST client interface using MicroProfile annotations to interact with the AI Endpoint, in a `AISentimentService.java` file:

```java
package com.ovhcloud.examples.aiendpoints.nlp.sentiment;

import org.eclipse.microprofile.rest.client.annotation.ClientHeaderParam;
import org.eclipse.microprofile.rest.client.inject.RegisterRestClient;
import jakarta.ws.rs.POST;
import jakarta.ws.rs.Path;

@Path("/api")
@RegisterRestClient(baseUri = "https://roberta-base-go-emotions.endpoints.kepler.ai.cloud.ovh.net")
@ClientHeaderParam(name = "Authorization", value = "Bearer ${ovhcloud.ai-endpoints.token}")
@ClientHeaderParam(name = "Content-Type", value = "application/json")
public interface AISentimentService {

  @POST
  @Path("text2emotions")
  EmotionEvaluation text2emotions(String text);

}
```

_We will use [Eclipse MicroProfile](https://microprofile.io/) and [Jakarta EE](https://jakarta.ee/) here._

_You can find the `EmotionEvaluation` class source code [here](https://github.com/ovh/public-cloud-examples/blob/main/ai/ai-endpoints/java-nlp/src/main/java/com/ovhcloud/examples/aiendpoints/nlp/sentiment/EmotionEvaluation.java)._

### Implement the REST resource

Now let's create the actual REST resource that uses the `AISentimentService`, in a `SentimentsAnalysisResource.java` file:

```java
package com.ovhcloud.examples.aiendpoints.nlp.sentiment;

import org.eclipse.microprofile.rest.client.inject.RestClient;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import jakarta.ws.rs.POST;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.Produces;
import jakarta.ws.rs.core.MediaType;

/**
 * Class to do textual sentiments analysis.
 */
@Path("/sentiment")
public class SentimentsAnalysisResource {

  private static final Logger _LOG = LoggerFactory.getLogger(SentimentsAnalysisResource.class);

  @RestClient
  private AISentimentService aiSentimentService;

  @POST
  @Produces(MediaType.TEXT_PLAIN)
  public String getSentiment(String text) {
    _LOG.info("Sentiment analysis for text: {}", text);

    return aiSentimentService.text2emotions(text).toEmoji();
  }
}
```

### Run the application

Now it's time to test the sentiment analyzer!

Start your Quarkus app in dev mode:

```bash
quarkus dev
```

Then, send a POST request to the analyzer endpoint (`http://localhost:8080/sentiment`), with a text as payload:

```
curl -X POST http://localhost:8080/sentiment -d "I feel great today!"
```

You should receive an emoji (or label) representing the sentiment of the text:

![image](/images/guides/public-cloud/ai-machine-learning/endpoints-tuto-04-sentiment-analyzer/sentiment-analysis.png)

## Conclusion

You have constructed a sentiment analyzer in Java using Quarkus for REST development and the `roberta-base-go_emotions` model to detect emotions in text.

## Going further

If you want to go further and deploy your Quarkus web app in the cloud, making your interface accessible to everyone, refer to the following articles and tutorials:

- [AI Deploy – Tutorial – Build & use a custom Docker image](/it/guides/public-cloud/ai-machine-learning/ai-deploy-build-use-custom-image.md)
- [AI Deploy – Tutorial – Deploy a Gradio app for sketch recognition](/it/guides/public-cloud/ai-machine-learning/ai-deploy-gradio-sketch-recognition.md)

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/it/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.

## Feedback

Please feel free to send us your questions, feedback, and suggestions regarding AI Endpoints and its features:

- In the #ai-endpoints channel of the OVHcloud [Discord server](https://discord.gg/ovhcloud), where you can engage with the community and OVHcloud team members.