Fossile of cermoney

we can use a combination of different sentiment analysis models to understand the machine's opinion about the answers. Here's a step-by-step approach: 1. **Preprocess the Data**: Clean and prepare the dataset of anonymous answers. 2. **Use Multiple Sentiment Analysis Models**: Apply various sentiment analysis models to the answers to get a comprehensive understanding of the sentiment. 3. **Aggregate the Results**: Combine the results from different models to form a consensus opinion. 4. **Generate a Rough Answer**: Based on the aggregated sentiment, generate a rough answer to the broadcast question. Here's an example of how you can implement this in Java: 1. **Add dependencies to your `pom.xml`**:    ```xml    <dependency>        <groupId>org.apache.opennlp</groupId>        <artifactId>opennlp-tools</artifactId>        <version>1.9.3</version>    </depen...

### 1. Define the Microservices - **Question Service**: Handles the posting of questions. - **Claim Service**: Handles the claiming of questions by users. - **Answer Service**: Handles the submission of answers. - **User Service**: Manages user information and authentication. ### 2. Technology Stack - **Spring Boot**: For building the microservices. - **Spring Cloud**: For managing microservice communication. - **Eureka**: For service discovery. - **Zuul**: For API Gateway. - **RabbitMQ**: For messaging between services. - **MySQL**: For database. ### 3. Example Code Snippets #### Question Service ```java @RestController @RequestMapping("/questions") public class QuestionController {     @Autowired     private QuestionService questionService;     @PostMapping     public ResponseEntity<Question> postQuestion(@RequestBody Question question) {         Question savedQuestion = questionService.saveQuestion(question);   ...