How to Add Sentiment Analysis to Your App in 5 Minutes (Free API)

Bob Steger — Tue, 03 Mar 2026 18:21:05 +0000

Every app that handles user-generated content eventually needs text analysis: customer reviews need sentiment scoring, blog platforms need readability checks, chat apps need toxicity filtering. Support tickets need classification. There seems like there's lots of need for these tools. So I built the Smart Text Toolkit, a single API with 14 text analysis endpoints. One subscription, one integration, and you've got sentiment analysis, readability scoring, summarization, keyword extraction, toxicity detection, PII redaction, and more.

In this tutorial, I'll show you how to integrate sentiment analysis into your app in under 5 minutes. Then I'll walk through the other endpoints so you can see what's available.

Output example

When you call the sentiment analysis endpoint and get back structured data like this:

{
  "sentiment": {
    "label": "positive",
    "score": 0.92,
    "scores": {
      "positive": 0.92,
      "negative": 0.03,
      "neutral": 0.05
    }
  },
  "processing_time_ms": 85
}

Step 1: Get Your API Key

Head to the RapidAPI and search for "Smart Text Toolkit."
You should be automatically subscribed to the free tier (you get 100 requests per month to test with).

Grab your API key from the dashboard. You'll need two values:

Your X-RapidAPI-Key
The host: smart-text-toolkit.p.rapidapi.com

If you don't see this, expand the AI-Powered category on the left panel and click the Analyze Sentiment endpoint.
Then, under the App tab in the middle of the window, you can get the API Key value.

Step 2: Call the API

Python code example

import requests

url = "https://smart-text-toolkit.p.rapidapi.com/api/v1/sentiment"

payload = {
    "text": "I absolutely love this product! The quality exceeded my expectations.",
    "granularity": "document"
}

headers = {
    "Content-Type": "application/json",
    "X-RapidAPI-Key": "YOUR_API_KEY_HERE",
    "X-RapidAPI-Host": "smart-text-toolkit.p.rapidapi.com"
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()

print(f"Sentiment: {data['sentiment']['label']}")
print(f"Confidence: {data['sentiment']['score']:.0%}")

Output:

Sentiment: positive
Confidence: 92%

JavaScript (Node.js)

const response = await fetch(
  "https://smart-text-toolkit.p.rapidapi.com/api/v1/sentiment",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "X-RapidAPI-Key": "YOUR_API_KEY_HERE",
      "X-RapidAPI-Host": "smart-text-toolkit.p.rapidapi.com",
    },
    body: JSON.stringify({
      text: "I absolutely love this product! The quality exceeded my expectations.",
      granularity: "document",
    }),
  }
);

const data = await response.json();
console.log(`Sentiment: ${data.sentiment.label} (${data.sentiment.score})`);

cURL

curl -X POST "https://smart-text-toolkit.p.rapidapi.com/api/v1/sentiment" \
  -H "Content-Type: application/json" \
  -H "X-RapidAPI-Key: YOUR_API_KEY_HERE" \
  -H "X-RapidAPI-Host: smart-text-toolkit.p.rapidapi.com" \
  -d '{"text": "I absolutely love this product!", "granularity": "document"}'

Step 3: Go Deeper with Sentence-Level Analysis (2 minutes)

The real power shows up when you switch to sentence-level granularity. You would use this for analyzing reviews, feedback, or support tickets where a single message contains mixed feelings.

payload = {
    "text": "The food was incredible and the atmosphere was perfect. "
            "However, the service was painfully slow and our waiter "
            "forgot our appetizers entirely.",
    "granularity": "sentence"
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()

for sentence in data["sentiment"]["sentences"]:
    emoji = "😊" if sentence["label"] == "positive" else "😠" if sentence["label"] == "negative" else "😐"
    print(f"{emoji} [{sentence['label']}] {sentence['text']}")

Output:

😊 [positive] The food was incredible and the atmosphere was perfect.
😠 [negative] However, the service was painfully slow and our waiter forgot our appetizers entirely.

Now you can pinpoint exactly what customers love and hate, and not just whether a review is good or bad.

Real-World Use Case: Review Analyzer

Here's a practical example — a function that analyzes a batch of product reviews and generates a summary:

import requests

API_URL = "https://smart-text-toolkit.p.rapidapi.com/api/v1"
HEADERS = {
    "Content-Type": "application/json",
    "X-RapidAPI-Key": "YOUR_API_KEY_HERE",
    "X-RapidAPI-Host": "smart-text-toolkit.p.rapidapi.com"
}

def analyze_reviews(reviews):
    results = {"positive": 0, "negative": 0, "neutral": 0}
    insights = []

    for review in reviews:
        # Get sentiment
        resp = requests.post(
            f"{API_URL}/sentiment",
            json={"text": review, "granularity": "document"},
            headers=HEADERS
        ).json()

        label = resp["sentiment"]["label"]
        results[label] += 1

        # Extract keywords from negative reviews
        if label == "negative":
            kw_resp = requests.post(
                f"{API_URL}/keywords",
                json={"text": review, "max_keywords": 5},
                headers=HEADERS
            ).json()

            top_issues = [kw["keyword"] for kw in kw_resp["keywords"][:3]]
            insights.append({
                "review": review[:80] + "...",
                "issues": top_issues
            })

    return {
        "total": len(reviews),
        "breakdown": results,
        "negative_insights": insights
    }

# Example usage
reviews = [
    "Absolutely love this product! Works exactly as described.",
    "Terrible quality. Broke after two days. Waste of money.",
    "It's okay, nothing special. Does what it says.",
    "The shipping was fast but the product itself is cheaply made.",
    "Best purchase I've made all year. Highly recommend!"
]

report = analyze_reviews(reviews)
print(f"Positive: {report['breakdown']['positive']}")
print(f"Negative: {report['breakdown']['negative']}")
print(f"Neutral:  {report['breakdown']['neutral']}")

if report["negative_insights"]:
    print("\nKey issues in negative reviews:")
    for item in report["negative_insights"]:
        print(f"  → {', '.join(item['issues'])}")

Notice how we combined two endpoints — sentiment and keywords — to not just detect negative reviews but understand why they're negative.

List of all available endpoints

All of these follow the same pattern (POST with JSON, get structured data in the response):

Endpoint	What It Does	Great For
`/sentiment`	Positive/negative/neutral scoring	Review analysis, social monitoring
`/readability`	Flesch-Kincaid, Gunning Fog, 5 more	CMS tools, educational platforms
`/summarize`	Extractive and abstractive summaries	News apps, document tools
`/keywords`	Keyword extraction + SEO density	SEO tools, content platforms
`/toxicity`	Multi-label toxicity detection	Chat moderation, community safety
`/language-detect`	50+ languages with confidence	i18n, content routing
`/grammar-check`	Spelling, grammar, style, punctuation	Writing assistants, editors
`/entities`	Named entity recognition (NER)	Data extraction, knowledge graphs
`/pii-detect`	Find and redact personal data	Compliance (GDPR/CCPA), privacy
`/text-compare`	Semantic + lexical similarity	Plagiarism check, dedup
`/classify`	Zero-shot text classification	Ticket routing, content tagging
`/paraphrase`	Rewrite with style control	Writing tools, content variation
`/seo-analyze`	Full SEO scoring + recommendations	Content marketing, blogging
`/emotion`	7 emotion categories (anger, joy...)	UX research, feedback analysis

I built these with performance in mind. Every response includes processing_time_ms value so you can monitor performance.

Another Example: PII (Personally Identifiable Information) Detection

Here's a quick look at the PII detection endpoint:

response = requests.post(
    f"{API_URL}/pii-detect",
    json={
        "text": "Contact John Smith at john.smith@acme.com "
               "or call 555-867-5309. His SSN is 123-45-6789.",
        "redact": True
    },
    headers=HEADERS
).json()

print(f"PII found: {response['total_entities']}")
print(f"Risk level: {response['risk_level']}")
print(f"\nRedacted: {response['redacted_text']}")

Output:

PII found: 4
Risk level: critical

Redacted: Contact ********** at **********************
or call ***-***-****. His SSN is ***-**-****.

One API call, and you've got GDPR-ready redaction.

Yet Another Example: Zero-Shot Classification

This one's my favorite. You provide any categories you want — no training, no ML expertise — and the API classifies text into them:

response = requests.post(
    f"{API_URL}/classify",
    json={
        "text": "I've been charged twice and I want a refund immediately.",
        "labels": ["billing", "technical_support", "returns", "general"],
    },
    headers=HEADERS
).json()

print(f"Category: {response['best_match']['label']}")
print(f"Confidence: {response['best_match']['score']:.0%}")

Output:

Category: billing
Confidence: 72%

Instant support ticket routing with zero training data.

Pricing

I wanted this to be accessible, so there's a free tier to start:

Basic: 100 requests/month (great for testing)
Pro ($9.99/mo): 5,000 requests
Ultra ($29.99/mo): 25,000 requests
Mega ($79.99/mo): 100,000 requests

All 14 endpoints included at every tier. No per-endpoint pricing games.

The Tech Behind It

For those curious about what's under the hood:

Framework: Python + FastAPI
Sentiment: RoBERTa-based model fine-tuned for sentiment
Summarization: DistilBART (CNN variant)
Toxicity: Detoxify (multi-label classifier)
PII: Microsoft Presidio
Classification: BART-large-MNLI (zero-shot)
Grammar: LanguageTool
NER + Keywords: spaCy
Similarity: Sentence-Transformers (MiniLM)

All models run on CPU — no GPU costs to pass on to you. Models are loaded once at startup and stay in memory, so there are no cold starts.

Try It Out

Search for "Smart Text Toolkit" on RapidAPI
Subscribe to the free tier
Test any endpoint using the built-in API playground
Integrate into your app using the code examples above

I'm a solo developer building this, so I genuinely appreciate any feedback. If there's an endpoint you'd want added or something isn't working right, drop a comment below or reach out through RapidAPI.

Happy building!

DEV Community: Bob Steger

How to Add Sentiment Analysis to Your App in 5 Minutes (Free API)

Output example

Step 1: Get Your API Key

Step 2: Call the API

Python code example

JavaScript (Node.js)

cURL

Step 3: Go Deeper with Sentence-Level Analysis (2 minutes)

Real-World Use Case: Review Analyzer

List of all available endpoints

Another Example: PII (Personally Identifiable Information) Detection

Yet Another Example: Zero-Shot Classification

Pricing

The Tech Behind It

Try It Out