Feature & Topic Sentiment Breakdown
Categorizes review text into specific features (e.g. pricing, UI, customer support, build quality) with sentiment scores.
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Customer review intelligence API for sentiment score, feature feedback, emotion tagging, and churn risk scoring.
https://test.rsflowhub.com/api/v1/ai/review-analyze
Product & App Store Review Sentiment Analysis API is a ready-to-use REST API on RSFlowHub for developers building web, mobile and AI agent applications. Send a JSON request to POST /api/v1/ai/review-analyze with your API key; RSFlowHub runs it on a tuned AI model (with automatic fallback to a backup provider) and returns a structured JSON result, usually within 1 to 3 seconds.
X-API-Key Header
curl -X POST "https://test.rsflowhub.com/api/v1/ai/review-analyze" \
-H "Content-Type: application/json" \
-H "X-API-Key: rfh_your_api_key" \
-d '{"review":"The food was absolutely delicious and the service was amazing! However, it was a bit overpriced.","rating":4,"context":"Italian restaurant in downtown New York"}'
<?php
$client = new \GuzzleHttp\Client();
$response = $client->post('https://test.rsflowhub.com/api/v1/ai/review-analyze', [
'headers' => [
'Content-Type' => 'application/json',
'X-API-Key' => 'rfh_your_api_key',
],
'json' => array (
'review' => 'The food was absolutely delicious and the service was amazing! However, it was a bit overpriced.',
'rating' => 4,
'context' => 'Italian restaurant in downtown New York',
),
]);
echo $response->getBody();
const response = await fetch('https://test.rsflowhub.com/api/v1/ai/review-analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': 'rfh_your_api_key'
},
body: JSON.stringify({"review":"The food was absolutely delicious and the service was amazing! However, it was a bit overpriced.","rating":4,"context":"Italian restaurant in downtown New York"})
});
const data = await response.json();
console.log(data);
import requests
url = "https://test.rsflowhub.com/api/v1/ai/review-analyze"
headers = {
"Content-Type": "application/json",
"X-API-Key": "rfh_your_api_key"
}
payload = {"review":"The food was absolutely delicious and the service was amazing! However, it was a bit overpriced.","rating":4,"context":"Italian restaurant in downtown New York"}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Categorizes review text into specific features (e.g. pricing, UI, customer support, build quality) with sentiment scores.
Flags reviews where positive text contradicts 1-star ratings or negative text accompanies 5-star ratings.
Measures customer emotional intensity, disappointment levels, and churn risk probability.
Tailored for Amazon, Google Maps, Apple App Store, Google Play Store, G2, and Trustpilot reviews.
See how developers integrate this API endpoint into production apps.
Analyze daily mobile app store reviews to automatically flag software bugs, login crashes, and feature requests.
Detect recurring product defect complaints across thousands of Amazon and Shopify buyer reviews.
Extract cleanliness, staff behavior, and location sentiment scores from Google Business reviews.
Comparing RSFlowHub's specialized API gateway vs raw multi-turn LLM prompts vs legacy regex rules.
| Evaluation Metric | RSFlowHub API | Raw LLM Prompts | Legacy Regex / Rules |
|---|---|---|---|
| Response Structure | Validated JSON envelope | Unstructured markdown or chat format | Brittle custom regex tuples |
| Setup Effort | One POST request, no prompts | Write and maintain prompts + output parsing | Fast, but rule lists miss context and sarcasm |
| Pricing Model | Flat 3 credit/request | Volatile token-based billing | High server maintenance overhead |
| Multi-Turn Hallucination | Zero (Deterministic validators) | Output format can drift between model versions | No semantic AI intelligence |
| Developer Integration | 3 lines cURL, PHP, JS, Python | Complex prompt engineering & retry loop | Fragile manual rule maintenance |
review
string
Raw review text payload to analyze.
rating
integer
Star rating value (1 to 5).
language
string
Review language code (e.g. "en", "es", "fr").
context
string
Product category or business domain context (e.g. "Italian restaurant in NYC").
Every endpoint returns the same envelope: success, request_id, data.result and meta.
{
"success": true,
"request_id": "req_3f9a2c7e-5b1d-4e8a-9c2f-7d6e1a0b4c8d",
"data": {
"result": {
"sentiment": "mixed",
"confidence": 0.88,
"language": "en",
"category": "product_quality",
"emotions": [
"satisfaction",
"frustration"
],
"key_topics": [
"delivery",
"build quality",
"battery life"
],
"positive_points": [
"Fast delivery",
"Great build quality"
],
"negative_points": [
"Short battery life"
],
"pain_points": [
"Battery does not last a full day"
],
"suggestions": [
"Improve battery capacity"
],
"urgency": "medium",
"attention_recommended": true
}
},
"meta": {
"version": "v1",
"timestamp": "2026-10-05T09:30:00Z"
}
}
Everything you need to know about integrating and using Product & App Store Review Sentiment Analysis API.
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