通过情感分析检测意图

情感分析可检查用户输入的内容,并确定其中主导性的主观意见,尤其是确定用户的态度是积极、消极还是中立的。 在发出检测意图请求时,您可以指定要执行情感分析,然后响应就会包含情感分析值。

Dialogflow 使用 Natural Language API 来执行此分析任务。 如需详细了解该 API 并阅读关于如何解读 Dialogflow 情感分析结果的文章,请访问:

支持的语言

如需查看受支持语言的列表,请参阅语言页面上的情感列。 如果您针对不受支持的语言请求情感分析,则检测意图请求不会失败,但 QueryResult.diagnostic_info 字段会包含错误信息。

准备工作

此功能仅适用于使用 API 与最终用户互动的情况。如果您使用的是集成服务,则可以跳过本指南。

在阅读本指南之前,请先完成以下事项:

  1. 阅读 Dialogflow 基础知识
  2. 执行设置步骤

创建代理

如果尚未创建代理,请立即创建一个:

  1. 转到 Dialogflow 控制台
  2. 如果系统要求登录 Dialogflow 控制台,请登录。如需了解详情,请参阅 Dialogflow 控制台概览
  3. 点击左侧边栏菜单中的创建代理 (Create Agent)。如果您已有其他代理,请点击代理名称,滚动到底部,然后点击创建新代理 (Create new agent)。
  4. 输入您的代理名称、默认语言和默认时区。
  5. 如果您已经创建了项目,请输入该项目。如果要允许 Dialogflow 控制台创建项目,请选择创建新 Google 项目 (Create a new Google project)。
  6. 点击创建 (Create) 按钮。

将示例文件导入代理

本指南中的步骤对您的代理进行了假设,因此您需要导入为本指南准备的代理。 导入时,这些步骤使用“恢复”(restore) 选项,该选项会覆盖所有代理设置、意图和实体。

如需导入文件,请按以下步骤操作:

  1. 下载 room-booking-agent.zip 文件。
  2. 转到 Dialogflow 控制台
  3. 选择您的代理。
  4. 点击代理名称旁边的设置 按钮。
  5. 选择导出和导入 (Export and Import) 标签页。
  6. 选择从 ZIP 文件恢复 (Restore from ZIP),然后按照说明恢复下载的 zip 文件。

用于情感分析的代理设置

您可以针对每个检测意图请求触发情感分析,也可以将代理配置为始终返回情感分析结果。

如需为所有查询启用情感分析,请执行以下操作:

  1. 转到 Dialogflow 控制台
  2. 选择一个代理。
  3. 点击代理名称旁边的设置 按钮。
  4. 选择高级 (Advanced) 标签页。
  5. 打开针对当前查询启用情感分析 (Enable sentiment analysis for the current query)。

使用 Dialogflow 模拟器

您可以通过 Dialogflow 模拟器与代理进行交互并接收情感分析结果:

  1. 输入“Thank you for helping me”。

  2. 请参阅模拟器底部的情感 (SENTIMENT) 部分。它应该显示积极的情感得分。

  3. 接下来,在模拟器中输入“It didn't work at all”。

  4. 请参阅模拟器底部的情感 (SENTIMENT) 部分。它应该显示消极的情感得分。

检测意图

REST 和命令行

调用 detectIntent 方法并提供 sentimentAnalysisRequestConfig 字段。

在使用下面的请求数据之前,请先进行以下替换:

  • project-id:您的 GCP 项目 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/project-id/agent/sessions/123456789:detectIntent

请求 JSON 正文:

{
  "queryParams": {
    "sentimentAnalysisRequestConfig": {
      "analyzeQueryTextSentiment": true
    }
  },
  "queryInput": {
    "text": {
      "text": "please reserve an amazing meeting room for six people",
      "languageCode": "en-US"
    }
  }
}

如需发送您的请求,请展开以下选项之一:

您应会收到如下所示的 JSON 响应:

{
  "responseId": "747ee176-acc5-46be-8d9a-b7ef9c2b9199",
  "queryResult": {
    "queryText": "please reserve an amazing meeting room for six people",
    "action": "room.reservation",
    "parameters": {
      "date": "",
      "duration": "",
      "guests": 6,
      "location": "",
      "time": ""
    },
    "fulfillmentText": "I can help with that. Where would you like to reserve a room?",
    ...
    "sentimentAnalysisResult": {
      "queryTextSentiment": {
        "score": 0.8,
        "magnitude": 0.8
      }
    }
  }
}

请注意,sentimentAnalysisResult 字段包含 scoremagnitude 值。

Java


import com.google.api.gax.rpc.ApiException;
import com.google.cloud.dialogflow.v2.DetectIntentRequest;
import com.google.cloud.dialogflow.v2.DetectIntentResponse;
import com.google.cloud.dialogflow.v2.QueryInput;
import com.google.cloud.dialogflow.v2.QueryParameters;
import com.google.cloud.dialogflow.v2.QueryResult;
import com.google.cloud.dialogflow.v2.SentimentAnalysisRequestConfig;
import com.google.cloud.dialogflow.v2.SessionName;
import com.google.cloud.dialogflow.v2.SessionsClient;
import com.google.cloud.dialogflow.v2.TextInput;
import com.google.common.collect.Maps;
import java.io.IOException;
import java.util.List;
import java.util.Map;

public class DetectIntentWithSentimentAnalysis {

  public static Map<String, QueryResult> detectIntentSentimentAnalysis(
      String projectId, List<String> texts, String sessionId, String languageCode)
      throws IOException, ApiException {
    Map<String, QueryResult> queryResults = Maps.newHashMap();
    // Instantiates a client
    try (SessionsClient sessionsClient = SessionsClient.create()) {
      // Set the session name using the sessionId (UUID) and projectID (my-project-id)
      SessionName session = SessionName.of(projectId, sessionId);
      System.out.println("Session Path: " + session.toString());

      // Detect intents for each text input
      for (String text : texts) {
        // Set the text (hello) and language code (en-US) for the query
        TextInput.Builder textInput =
            TextInput.newBuilder().setText(text).setLanguageCode(languageCode);

        // Build the query with the TextInput
        QueryInput queryInput = QueryInput.newBuilder().setText(textInput).build();

        //
        SentimentAnalysisRequestConfig sentimentAnalysisRequestConfig =
            SentimentAnalysisRequestConfig.newBuilder().setAnalyzeQueryTextSentiment(true).build();

        QueryParameters queryParameters =
            QueryParameters.newBuilder()
                .setSentimentAnalysisRequestConfig(sentimentAnalysisRequestConfig)
                .build();
        DetectIntentRequest detectIntentRequest =
            DetectIntentRequest.newBuilder()
                .setSession(session.toString())
                .setQueryInput(queryInput)
                .setQueryParams(queryParameters)
                .build();

        // Performs the detect intent request
        DetectIntentResponse response = sessionsClient.detectIntent(detectIntentRequest);

        // Display the query result
        QueryResult queryResult = response.getQueryResult();

        System.out.println("====================");
        System.out.format("Query Text: '%s'\n", queryResult.getQueryText());
        System.out.format(
            "Detected Intent: %s (confidence: %f)\n",
            queryResult.getIntent().getDisplayName(), queryResult.getIntentDetectionConfidence());
        System.out.format("Fulfillment Text: '%s'\n", queryResult.getFulfillmentText());
        System.out.format(
            "Sentiment Score: '%s'\n",
            queryResult.getSentimentAnalysisResult().getQueryTextSentiment().getScore());

        queryResults.put(text, queryResult);
      }
    }
    return queryResults;
  }
}

Node.js

// Imports the Dialogflow client library
const dialogflow = require('@google-cloud/dialogflow').v2;

// Instantiate a DialogFlow client.
const sessionClient = new dialogflow.SessionsClient();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = 'ID of GCP project associated with your Dialogflow agent';
// const sessionId = `user specific ID of session, e.g. 12345`;
// const query = `phrase(s) to pass to detect, e.g. I'd like to reserve a room for six people`;
// const languageCode = 'BCP-47 language code, e.g. en-US';

// Define session path
const sessionPath = sessionClient.projectAgentSessionPath(
  projectId,
  sessionId
);

async function detectIntentandSentiment() {
  // The text query request.
  const request = {
    session: sessionPath,
    queryInput: {
      text: {
        text: query,
        languageCode: languageCode,
      },
    },
    queryParams: {
      sentimentAnalysisRequestConfig: {
        analyzeQueryTextSentiment: true,
      },
    },
  };

  // Send request and log result
  const responses = await sessionClient.detectIntent(request);
  console.log('Detected intent');
  const result = responses[0].queryResult;
  console.log(`  Query: ${result.queryText}`);
  console.log(`  Response: ${result.fulfillmentText}`);
  if (result.intent) {
    console.log(`  Intent: ${result.intent.displayName}`);
  } else {
    console.log('  No intent matched.');
  }
  if (result.sentimentAnalysisResult) {
    console.log('Detected sentiment');
    console.log(
      `  Score: ${result.sentimentAnalysisResult.queryTextSentiment.score}`
    );
    console.log(
      `  Magnitude: ${result.sentimentAnalysisResult.queryTextSentiment.magnitude}`
    );
  } else {
    console.log('No sentiment Analysis Found');
  }

Python

def detect_intent_with_sentiment_analysis(project_id, session_id, texts,
                                          language_code):
    """Returns the result of detect intent with texts as inputs and analyzes the
    sentiment of the query text.

    Using the same `session_id` between requests allows continuation
    of the conversation."""
    import dialogflow_v2 as dialogflow
    session_client = dialogflow.SessionsClient()

    session_path = session_client.session_path(project_id, session_id)
    print('Session path: {}\n'.format(session_path))

    for text in texts:
        text_input = dialogflow.types.TextInput(
            text=text, language_code=language_code)

        query_input = dialogflow.types.QueryInput(text=text_input)

        # Enable sentiment analysis
        sentiment_config = dialogflow.types.SentimentAnalysisRequestConfig(
            analyze_query_text_sentiment=True)

        # Set the query parameters with sentiment analysis
        query_params = dialogflow.types.QueryParameters(
            sentiment_analysis_request_config=sentiment_config)

        response = session_client.detect_intent(
            session=session_path, query_input=query_input,
            query_params=query_params)

        print('=' * 20)
        print('Query text: {}'.format(response.query_result.query_text))
        print('Detected intent: {} (confidence: {})\n'.format(
            response.query_result.intent.display_name,
            response.query_result.intent_detection_confidence))
        print('Fulfillment text: {}\n'.format(
            response.query_result.fulfillment_text))
        # Score between -1.0 (negative sentiment) and 1.0 (positive sentiment).
        print('Query Text Sentiment Score: {}\n'.format(
            response.query_result.sentiment_analysis_result
            .query_text_sentiment.score))
        print('Query Text Sentiment Magnitude: {}\n'.format(
            response.query_result.sentiment_analysis_result
            .query_text_sentiment.magnitude))