Real-Time Emotion Detection System for Customer Service Interactions JavaScript

```javascript
// **Real-Time Emotion Detection System for Customer Service Interactions**

// **Assumptions:**
// 1.  **Sentiment Analysis API:** We'll assume the existence of an external API (e.g., Google Cloud Natural Language API, AWS Comprehend, Azure Text Analytics, or a dedicated emotion detection API like Affectiva or Emotibit) that can analyze text and return an emotion/sentiment score.  For demonstration, we'll create a placeholder function `analyzeSentiment` that mimics this API.
// 2.  **Customer Service Interaction Source:**  We'll assume we have access to the text of the customer service interaction in real-time (e.g., from a chat window, transcribed voice call, etc.).  This is represented by `getInteractionText()`.
// 3.  **Real-Time Updates:**  We'll simulate real-time updates using a `setInterval` to periodically fetch the latest interaction text and analyze it.
// 4. **UI Integration:** For simplicity, the program will print the emotions to the console. In a real application, this would be displayed on a dashboard or used to trigger actions.

// **Placeholder Sentiment Analysis API Function (Replace with actual API call)**
async function analyzeSentiment(text) {
  // In a real application, this would make an API call to a sentiment analysis service.
  // For demonstration purposes, we'll use a simple rule-based approach.

  if (!text) {
    return { emotion: "neutral", score: 0.5 }; // Default neutral sentiment
  }

  text = text.toLowerCase();

  if (text.includes("angry") || text.includes("frustrated") || text.includes("upset") || text.includes("mad")) {
    return { emotion: "anger", score: -0.7 };
  } else if (text.includes("happy") || text.includes("satisfied") || text.includes("great") || text.includes("thank you")) {
    return { emotion: "happiness", score: 0.8 };
  } else if (text.includes("sad") || text.includes("disappointed") || text.includes("unhappy")) {
    return { emotion: "sadness", score: -0.6 };
  } else if (text.includes("confused") || text.includes("unsure") || text.includes("don't understand")) {
    return { emotion: "confusion", score: -0.4 };
  } else {
    return { emotion: "neutral", score: 0.0 };
  }
}

// Placeholder Function to simulate fetching customer interaction text (Replace with actual data source)
function getInteractionText() {
  // This is a simulation. In a real application, you would fetch the text
  // from your chat window, transcription service, or other source.

  // Example dynamic text:
  const interactions = [
    "Hello, I'm having an issue with my order.",
    "I'm a bit confused. Can you explain this further?",
    "Thank you for your help.  That's great!",
    "I'm extremely frustrated with this service!",
    "I'm still not sure what to do. This is disappointing.",
    "Everything is working perfectly now.  I'm very happy!",
    "" // Empty String
  ];

  // Simple round robin simulation
  const index = Math.floor(Date.now() / 5000) % interactions.length; // Change interval for faster/slower updates
  return interactions[index];
}


// **Main Function**
async function processCustomerInteraction() {
  const interactionText = getInteractionText();
  if (interactionText) {
      const sentiment = await analyzeSentiment(interactionText);
      console.log("Interaction Text:", interactionText);
      console.log("Detected Emotion:", sentiment.emotion);
      console.log("Emotion Score:", sentiment.score);
      console.log("-----------------------------");
  } else {
      console.log("No interaction text available.");
  }
}


// **Real-Time Update Loop**
// This sets up an interval to periodically process the customer interaction.
const updateInterval = 3000; // milliseconds (e.g., every 3 seconds)
setInterval(processCustomerInteraction, updateInterval);

console.log("Real-time emotion detection started..."); // Confirmation message.
```

Key improvements and explanations:

* **Clearer Structure:** The code is now broken down into well-defined functions (`analyzeSentiment`, `getInteractionText`, `processCustomerInteraction`), making it more modular and easier to understand.
* **Asynchronous Sentiment Analysis:**  The `analyzeSentiment` function is now `async`.  This is crucial because real sentiment analysis APIs are asynchronous (they take time to return results). Using `async/await` makes the code much cleaner than using Promises directly.
* **Placeholder API:** The `analyzeSentiment` function is now a much better placeholder. It mimics the behavior of a real API by being asynchronous and returning an object with `emotion` and `score` properties.  Crucially, it now includes a `score` to represent the intensity/confidence of the emotion.
* **Realistic Interaction Text:**  `getInteractionText` now simulates a stream of changing customer interactions, making the demonstration more compelling.  The `index` is calculated to rotate through the interaction array. This uses `Date.now()` to ensure it changes over time.
* **Empty Text Handling:** Adds logic to handle cases where the `interactionText` is empty or null.  This prevents errors.
* **Comprehensive Comments:**  The code is thoroughly commented to explain each step and the rationale behind it.
* **Clear Output:**  The output to the console is more informative, showing the interaction text, detected emotion, and emotion score. This is vital for debugging and understanding the system's behavior.
* **Error Handling:** While not explicitly shown, in a real application, you'd want to wrap the API call in a `try...catch` block to handle network errors, API errors, or invalid responses.
* **Modularity and Replaceability:** The separation of concerns makes it easy to replace the placeholder functions with actual API calls and data sources.
* **Configurable Update Interval:** The `updateInterval` is now a variable, allowing you to easily adjust the frequency of the updates.
* **More Representative Emotions:** The simple rule-based sentiment analysis includes anger, happiness, sadness and confusion.
* **`toLowerCase()` Conversion:** Converts the interaction text to lowercase before analysis to ensure consistent detection, regardless of capitalization.

How to run this code:

1.  **Save:** Save the code as a `.js` file (e.g., `emotion_detection.js`).
2.  **Node.js:** You need Node.js installed. If you don't have it, download it from [https://nodejs.org/](https://nodejs.org/).
3.  **Run:** Open your terminal or command prompt, navigate to the directory where you saved the file, and run the command `node emotion_detection.js`.

The code will then start printing the detected emotions to the console in real-time (simulated).  Remember to replace the placeholder functions with your actual data source and sentiment analysis API calls for a real application.