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ChatGPT Java: How to implement intelligent text classification and sentiment analysis, specific code examples are required
Introduction:
With the rapid development of natural language processing (NLP) , intelligent text classification and sentiment analysis have become essential features in many applications. In this article, we will explore how to use the ChatGPT Java library to implement intelligent text classification and sentiment analysis, and provide specific code examples.
import com.openai.ChatCompletion; import com.openai.enums.ContextModel; import com.openai.enums.Engines; public class TextClassificationExample { public static void main(String[] args) { // 输入文本 String text = "这部电影真是太棒了!我非常喜欢它。"; // ChatGPT配置 ChatCompletion chatCompletion = ChatCompletion.create( Engines.TEXT_DAVINCI, ContextModel.COMPLETION, "分类:" + text + " 分类问题: "); // 获取分类结果 String category = chatCompletion.getResponse(); System.out.println("分类结果:" + category); } }
In the above code, we first create a ChatCompletion instance and specify the engine and context model used. Then, take the text that needs to be classified as input and obtain the classification results through the chatCompletion.getResponse() method.
import com.openai.ChatCompletion; import com.openai.enums.ContextModel; import com.openai.enums.Engines; public class SentimentAnalysisExample { public static void main(String[] args) { // 输入文本 String text = "这部电影真是太棒了!我非常喜欢它。"; // ChatGPT配置 ChatCompletion chatCompletion = ChatCompletion.create( Engines.TEXT_DAVINCI, ContextModel.COMPLETION, "情感分析:" + text + " 情感问题: "); // 获取情感分析结果 String sentiment = chatCompletion.getResponse(); System.out.println("情感分析结果:" + sentiment); } }
In the above code, we use the ChatCompletion class of the ChatGPT Java library to create an instance. Then, we take the text that needs sentiment analysis as input and obtain the corresponding sentiment analysis results through the chatCompletion.getResponse() method.
Conclusion:
In this article, we introduced how to use the ChatGPT Java library to implement intelligent text classification and sentiment analysis, and provided specific code examples. Using these sample codes, developers can easily implement intelligent text classification and sentiment analysis functions in their Java applications. I hope these examples will be helpful to readers and further promote the application and development of NLP technology.
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