Leverage Azure AI for Sentiment Analysis in Your Next.js Application

By leveraging sentiment analysis in your Sitecore applications, you can gain valuable insights, enhance customer engagement, and drive business growth through informed decision-making and personalized user experiences.

Key Benefits of Using Sentiment Analysis in a Sitecore Project:

Understanding Sentiment: By analyzing user comments, reviews, and feedback, you can gauge customer sentiment and understand how users feel about your products, services, or content.

Tailored Content: Use sentiment data to personalize content based on user emotions. For instance, if a user consistently leaves positive comments about a particular type of content, you can recommend similar content to them.

Real-time Feedback: Quickly identify negative sentiments in user feedback and respond proactively to address concerns, improving customer satisfaction.

Feedback Analysis: Analyze sentiment in feedback to identify strengths and weaknesses in your content. Use this information to improve content quality and relevance.

Automated Workflows: Set up automated workflows that trigger actions based on sentiment analysis results, such as sending follow-up emails to users who leave negative feedback.

Market Trends: Stay ahead of market trends by analyzing sentiment around industry-related topics and adapting your strategies accordingly.

Use Case — We recently completed a POC for one of our clients, focusing on analyzing the comments and feedback received through their feedback form. The goal was to improve content and personalize the user experience based on their interests.

Here’s an example of how we have implemented sentiment analysis in a Next.js project:

Prerequisites

1. Azure Subscription: Ensure you have an Azure account and a Text Analytics resource set up. You should have the endpoint URL and subscription key.
2. Next.js Setup: Ensure you have a Next.js project set up. You can create a new Next.js project if needed.

Step 1 — Create an Azure Text Analytics resource and obtain the subscription key and endpoint.

Step 2 — Assuming you have already setup the Next.js project. Now install the axios package in your project for making HTTP requests.

npm install axios

Step 3 — Create a Custom Processor Module file: lib/sentimentProcessor.js

// lib/sentimentProcessor.js
import axios from 'axios';

const endpoint = process.env.AZURE_TEXT_ANALYTICS_ENDPOINT;
const subscriptionKey = process.env.AZURE_TEXT_ANALYTICS_KEY;

export async function analyzeSentiment(text) {
  const url = `${endpoint}/text/analytics/v3.1/sentiment`;

  try {
    const response = await axios.post(
      url,
      { documents: [{ id: '1', language: 'en', text }] },
      {
        headers: {
          'Ocp-Apim-Subscription-Key': subscriptionKey,
          'Content-Type': 'application/json'
        }
      }
    );

    const sentiment = response.data.documents[0].sentiment;
    return { sentiment, success: true };
  } catch (error) {
    console.error('Error analyzing sentiment:', error);
    return { sentiment: null, success: false, error: error.message };
  }
}

Step 4 — Create API Route file at pages/api/sentiment.js

// pages/api/sentiment.js
import { analyzeSentiment } from '../../lib/sentimentProcessor';

export default async function handler(req, res) {
  if (req.method !== 'POST') {
    return res.status(405).send({ message: 'Only POST requests are allowed' });
  }

  const { text } = req.body;

  if (!text) {
    return res.status(400).send({ message: 'Text is required' });
  }

  const result = await analyzeSentiment(text);

  if (result.success) {
    res.status(200).json({ sentiment: result.sentiment });
  } else {
    res.status(500).send({ message: 'Error analyzing sentiment', error: result.error });
  }
}

Step 5 — Configure Environment Variables.

AZURE_TEXT_ANALYTICS_ENDPOINT=https://your-text-analytics-endpoint.cognitiveservices.azure.com/
AZURE_TEXT_ANALYTICS_KEY=your-subscription-key

Step 6 — Create the Sentiment Analysis Feedback Form.

// pages/index.js
import { useState } from 'react';
import axios from 'axios';

export default function Home() {
  const [text, setText] = useState('');
  const [sentiment, setSentiment] = useState(null);
  const [error, setError] = useState(null);

  const handleSubmit = async (event) => {
    event.preventDefault();

    try {
      const response = await axios.post('/api/sentiment', { text });
      setSentiment(response.data.sentiment);
      setError(null);
    } catch (err) {
      setError(err.response?.data?.message || 'An error occurred');
      setSentiment(null);
    }
  };

  return (
    <div>
      <h1>Sentiment Analysis</h1>
      <form onSubmit={handleSubmit}>
        <textarea
          value={text}
          onChange={(e) => setText(e.target.value)}
          rows="4"
          cols="50"
          placeholder="Please input your comment to analyze."
        />
        <button type="submit">Analyze Sentiment</button>
      </form>
      {sentiment && <p>Sentiment: {sentiment}</p>}
      {error && <p style={{ color: 'red' }}>{error}</p>}
    </div>
  );
}

That’s it. To submit sentiment data obtained from Azure Text Analytics to Sitecore, you can use Sitecore’s RESTful API or GraphQL whatever is applicable in your case.

This setup allows you to integrate Azure Text Analytics for sentiment analysis into your Next.js project, enhancing your application’s ability to understand and respond to user feedback.

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Thank you!