Detect intent with sentiment analysis

Sentiment analysis inspects user input and identifies the prevailing subjective opinion, especially to determine a user's attitude as positive, negative, or neutral. When making a detect intent request, you can specify that sentiment analysis be performed, and the response will contain sentiment analysis values.

The Natural Language API is used by Dialogflow to perform this analysis. For more information on that API and documentation on interpreting Dialogflow sentiment analysis results:

Supported languages

For a list of supported languages, see the sentiment column on the languages page. If you request sentiment analysis for an unsupported language, your detect intent request does not fail, but the QueryResult.diagnostic_info field contains error information.

Before you begin

You should do the following before reading this guide:

  1. Read Dialogflow basics.
  2. Perform setup steps.

Create an agent

The steps in this guide make assumptions about your agent, so it's best to start with a new agent. You should delete any existing agent for your project before creating a new one. To delete an existing agent:

  1. Go to the Dialogflow Console.
  2. If requested, sign in to the Dialogflow Console. See Dialogflow console overview for more information.
  3. Select the agent you wish to delete.
  4. Click the settings settings button next to the agent's name.
  5. Scroll down to the bottom of the General settings tab.
  6. Click Delete this agent.
  7. Enter DELETE in the text field.
  8. Click Delete.

To create an agent:

  1. Go to the Dialogflow Console.
  2. If requested, sign in to the Dialogflow Console. See Dialogflow console overview for more information.
  3. Click Create Agent in the left sidebar menu. (If you already have other agents, click the agent name, scroll to the bottom and click Create new agent.)
  4. Enter your agent's name, default language, and default time zone.
  5. If you have already created a project, enter that project. If you want to allow the Dialogflow Console to create the project, select Create a new Google project.
  6. Click the Create button.

Import the example file to your agent

Importing will add intents and entities to your agent. If any existing intents or entities have the same name as those in the imported file, they will be replaced.

To import the file, follow these steps:

  1. Download the file
  2. Go to the Dialogflow Console
  3. Select your agent
  4. Click the settings settings button next to the agent name
  5. Select the Export and Import tab
  6. Select Import From Zip and import the zip file that you downloaded

Agent settings for sentiment analysis

You can trigger sentiment analysis per detect intent request, or you can configure your agent to always return sentiment analysis results.

To enable sentiment analysis for all queries:

  1. Go to the Dialogflow Console.
  2. Select an agent.
  3. Click the settings settings button next to the agent name.
  4. Select the Advanced tab.
  5. Toggle Enable sentiment analysis for the current query on.

Use the Dialogflow simulator

You can interact with the agent and receive sentiment analysis results via the Dialogflow simulator:

  1. Type "please reserve an amazing meeting room for six people" in the simulator.

  2. See the SENTIMENT section at the bottom of the simulator.

Detect intent


Call the detectIntent method and provide the sentimentAnalysisRequestConfig field.

Before using any of the request data below, make the following replacements:

  • project-id: your GCP project ID

HTTP method and URL:


Request JSON body:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

  "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

Notice that the sentimentAnalysisResult field contains score and magnitude values.


 * Returns the result of detect intent with texts as inputs.
 * <p>Using the same `session_id` between requests allows continuation of the conversation.
 * @param projectId    Project/Agent Id.
 * @param texts        The text intents to be detected based on what a user says.
 * @param sessionId    Identifier of the DetectIntent session.
 * @param languageCode Language code of the query.
 * @return The QueryResult for each text in query.
public static Map<String, QueryResult>  detectIntentSentimentAnalysis(
    String projectId,
    List<String> texts,
    String sessionId,
    String languageCode) throws Exception {
  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
      Builder textInput = TextInput.newBuilder().setText(text).setLanguageCode(languageCode);

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

      SentimentAnalysisRequestConfig sentimentAnalysisRequestConfig =

      QueryParameters queryParameters =
      DetectIntentRequest detectIntentRequest =

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

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

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

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


// Imports the Dialogflow client library
const dialogflow = require('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.sessionPath(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`);
      `  Score: ${result.sentimentAnalysisResult.queryTextSentiment.score}`
      `  Magnitude: ${result.sentimentAnalysisResult.queryTextSentiment.magnitude}`
  } else {
    console.log(`No sentiment Analysis Found`);


def detect_intent_with_sentiment_analysis(project_id, session_id, texts,
    """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(

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

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

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

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