Free, Unlimited Sentiment Analysis API
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In this guide, you will learn how to integrate sentiment analysis capabilities into your app for free, without developer accounts or API keys. Instead of traditional NLP APIs, we'll use Large Language Models (LLMs) through Puter.js for accurate, context-aware sentiment detection. LLMs excel at sentiment analysis because they understand nuance, sarcasm, and context—producing more accurate results than traditional rule-based sentiment APIs.
Puter.js uses the User-Pays model, where users of your application cover their own AI costs. This means you as a developer don't pay anything for your users' usage, making your app practically free to run. You can scale to unlimited users and pay nothing for the sentiment analysis usage.
Getting Started
Add Puter.js to your project with a single line:
<script src="https://js.puter.com/v2/"></script>
That's it, you're ready to start adding sentiment analysis to your application.
Example 1: Basic sentiment analysis
You can use the puter.ai.chat() function to access more than 500 large language models for sentiment analysis. Here's an example using GPT-5 nano to analyze text sentiment.
const text = "I absolutely love this product! It exceeded all my expectations.";
const prompt = `Analyze the sentiment of the following text and respond with only one word: positive, negative, or neutral.\n\nText: ${text}`;
puter.ai.chat(prompt, { model: "gpt-5-nano" })
.then(sentiment => {
console.log(sentiment);
});
Full code example:
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
const text = "I absolutely love this product! It exceeded all my expectations.";
const prompt = `Analyze the sentiment of the following text and respond with only one word: positive, negative, or neutral.\n\nText: ${text}`;
puter.print("Analyzing sentiment...");
puter.ai.chat(prompt, { model: "gpt-5-nano" })
.then(sentiment => {
puter.print(`Sentiment: ${sentiment}`);
});
</script>
</body>
</html>
Example 2: Interactive sentiment analyzer
The following example shows how to create an interactive sentiment analyzer with a simple UI.
<html>
<head>
<style>
body { font-family: Arial, sans-serif; max-width: 600px; margin: 0 auto; padding: 20px;}
textarea { width: 100%; height: 100px; margin: 10px 0; padding: 10px; border: 1px solid #ccc; border-radius: 4px; }
button { margin: 5px; padding: 10px 15px; border: 1px solid #ccc; border-radius: 4px; cursor: pointer; background-color: #007bff; color: white; border: none; }
button:hover { background-color: #0056b3; }
.result { margin: 10px 0; padding: 10px; background-color: #f8f9fa; border-radius: 4px; min-height: 50px; }
.sentiment-positive { background-color: #d4edda; color: #155724; }
.sentiment-negative { background-color: #f8d7da; color: #721c24; }
.sentiment-neutral { background-color: #fff3cd; color: #856404; }
</style>
</head>
<body>
<script src="https://js.puter.com/v2/"></script>
<h1>Sentiment Analyzer</h1>
<textarea id="text-input" placeholder="Enter text to analyze sentiment...">This product is amazing! I'm so happy with my purchase.</textarea>
<div>
<button onclick="analyzeSentiment()">Analyze Sentiment</button>
</div>
<div id="status"></div>
<div id="result" class="result"></div>
<script>
const textInput = document.getElementById('text-input');
const statusDiv = document.getElementById('status');
const resultDiv = document.getElementById('result');
async function analyzeSentiment() {
const text = textInput.value.trim();
if (!text) {
statusDiv.textContent = 'Please enter some text first!';
return;
}
statusDiv.textContent = 'Analyzing sentiment...';
resultDiv.textContent = '';
resultDiv.className = 'result';
try {
const prompt = `Analyze the sentiment of the following text and respond with only one word: positive, negative, or neutral.\n\nText: ${text}`;
const sentiment = await puter.ai.chat(prompt, {
model: "gpt-5-nano"
});
const cleanSentiment = sentiment.message.content.toLowerCase().trim();
statusDiv.textContent = '';
resultDiv.textContent = `Sentiment: ${cleanSentiment}`;
resultDiv.className = `result sentiment-${cleanSentiment}`;
} catch (error) {
statusDiv.textContent = `Error: ${error.message}`;
}
}
</script>
</body>
</html>
Example 3: Detailed sentiment analysis with JSON output
For more detailed sentiment analysis including confidence scores and reasoning, you can prompt the model to return structured JSON data:
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
(async () => {
const text = "The service was okay, but the food was disappointing. However, the staff were incredibly friendly.";
const prompt = `Analyze the sentiment of the following text. Return your response as a JSON object with these fields:
- sentiment: "positive", "negative", or "neutral"
- confidence: a number between 0 and 1
- reasoning: brief explanation of the sentiment
Text: ${text}
Respond only with valid JSON, no additional text.`;
puter.print("Analyzing sentiment...<br><br>");
const response = await puter.ai.chat(prompt, {
model: 'gpt-5-nano'
});
try {
const analysis = JSON.parse(response);
puter.print(`<strong>Sentiment:</strong> ${analysis.sentiment}<br>`);
puter.print(`<strong>Confidence:</strong> ${(analysis.confidence * 100).toFixed(1)}%<br>`);
puter.print(`<strong>Reasoning:</strong> ${analysis.reasoning}<br>`);
} catch (e) {
puter.print(`Raw response: ${response}`);
}
})();
</script>
</body>
</html>
Example 4: Batch sentiment analysis
Analyze multiple texts at once for efficient processing:
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
(async () => {
const reviews = [
"Best purchase I've ever made!",
"Terrible quality, don't waste your money.",
"It's fine, nothing special.",
"Exceeded my expectations in every way!",
"Not worth the price at all."
];
puter.print("<h3>Batch Sentiment Analysis</h3>");
for (const review of reviews) {
const prompt = `Analyze the sentiment of the following text and respond with only one word: positive, negative, or neutral.\n\nText: ${review}`;
const sentiment = await puter.ai.chat(prompt, {
model: 'gpt-5-nano'
});
puter.print(`<div style="margin: 10px 0; padding: 10px; background: #f5f5f5; border-radius: 4px;">
<strong>Review:</strong> "${review}"<br>
<strong>Sentiment:</strong> ${sentiment.message.content.toLowerCase().trim()}
</div>`);
}
})();
</script>
</body>
</html>
Conclusion
Using Puter.js, you can add LLM-powered sentiment analysis capabilities to your application without setting up API keys or managing NLP services. LLMs provide context-aware sentiment detection that understands nuance, sarcasm, and mixed sentiments better than traditional rule-based approaches. Thanks to the User-Pays model, you can add this feature for free to your application, since your users cover their own AI usage, not you as the developer.
You can find more details about Puter.js AI API in the documentation.
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