System design and implementation of sentiment analysis by docking Baidu AI interface in Java language

System design and implementation of emotional analysis by docking Baidu AI interface in Java language

Abstract:
With the continuous development and application of artificial intelligence technology, emotions Analysis has gradually become an important area of ​​research. This article designs and implements a sentiment analysis system based on Java language and with the support of Baidu AI platform. By integrating with Baidu's AI interface, the system can quickly and accurately perform sentiment analysis on texts and give positive or negative emotional tendency judgments.

1. Introduction
Sentiment analysis, also called opinion mining or sentiment computing, refers to the method of analyzing emotions, tendencies and subjective information in texts through natural language processing, text mining and other technologies. Sentiment analysis is widely used in many fields, such as public opinion analysis, market research, airline service quality evaluation, etc. This article implements a simple and practical sentiment analysis system by connecting with the Baidu AI platform interface.

2. System design

1. System architecture
   The system architecture is as follows:

   User interface
   ————↗—— ——
   | |
   Business logic processing layer
   ————↗————
   | |
   AI interface layer
   ————↗————
   | |
   Data storage layer

2. Main module
   User interface: Provides the function for users to input text, and passes user input content to the business logic processing layer.

Business logic processing layer: receives the text input by the user, calls the Baidu sentiment analysis API through the AI ​​interface layer, obtains the analysis results, and gives a positive or negative emotional tendency judgment.

AI interface layer: Connect with Baidu Sentiment Analysis API through Baidu AI SDK, pass the text input by the user to the API for sentiment analysis, and obtain the returned analysis results.

Data storage layer: Save the text and sentiment analysis results entered by the user into the database to facilitate subsequent query and analysis.

3. System implementation

1. Environment preparation
   First, you need to register an account on the Baidu AI platform and create an application to obtain the API Key and Secret Key.

Secondly, you need to build a Java development environment and import the relevant Baidu AI SDK.

2. Code example

import com.baidu.aip.nlp.AipNlp;
import org.json.JSONObject;

public class SentimentAnalysis {

// 设置APPID/AK/SK
public static final String APP_ID = "your_app_id";
public static final String API_KEY = "your_api_key";
public static final String SECRET_KEY = "your_secret_key";

public static void main(String[] args) {
    // 初始化一个AipNlp
    AipNlp client = new AipNlp(APP_ID, API_KEY, SECRET_KEY);

    // 设置参数
    HashMap<String, Object> options = new HashMap<String, Object>();
    options.put("model", "CNN");

    // 调用情感分析接口
    String text = "这部电影真是太棒了！";
    JSONObject result = client.sentimentClassify(text, options);
    System.out.println(result);
}

}

3. Run and debug
   Copy the above code to the Java development environment, and replace the API Key and Secret Key with the ones you created on the Baidu AI platform The key of the application.

Run the program and enter the test text. The program will call Baidu Sentiment Analysis API to perform sentiment analysis and print out the analysis results.

4. System Testing and Evaluation
By inputting different texts, the accuracy of sentiment analysis of the system is tested and the system is evaluated. The results can be compared with manual analysis and errors can be analyzed and improved.

5. Summary
This article is based on Java language and uses Baidu AI interface to implement a simple and practical sentiment analysis system. The system can perform sentiment analysis on text and give a positive or negative emotional tendency judgment. This system has high accuracy and practicality, and has certain application value in fields such as public opinion analysis and market research.

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