Multi-Dimensional Sentiment & Emotion
Detects positive, negative, neutral, and emotional sub-tones such as excitement, curiosity, or frustration.
Loading RSFlowHub...
Social media comment intelligence API for sentiment, intent, reply necessity, and priority scoring.
https://test.rsflowhub.com/api/v1/ai/comment-analyze
Social Media Comment & Tone 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/comment-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/comment-analyze" \
-H "Content-Type: application/json" \
-H "X-API-Key: rfh_your_api_key" \
-d '{"comment":"Do you ship to Mumbai? I would love to order this today!","platform":"Instagram","post_context":"Photo of our new handcrafted leather boots collection","business_context":"We are an artisanal e-commerce shop delivering worldwide. Shipping is free within India."}'
<?php
$client = new \GuzzleHttp\Client();
$response = $client->post('https://test.rsflowhub.com/api/v1/ai/comment-analyze', [
'headers' => [
'Content-Type' => 'application/json',
'X-API-Key' => 'rfh_your_api_key',
],
'json' => array (
'comment' => 'Do you ship to Mumbai? I would love to order this today!',
'platform' => 'Instagram',
'post_context' => 'Photo of our new handcrafted leather boots collection',
'business_context' => 'We are an artisanal e-commerce shop delivering worldwide. Shipping is free within India.',
),
]);
echo $response->getBody();
const response = await fetch('https://test.rsflowhub.com/api/v1/ai/comment-analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': 'rfh_your_api_key'
},
body: JSON.stringify({"comment":"Do you ship to Mumbai? I would love to order this today!","platform":"Instagram","post_context":"Photo of our new handcrafted leather boots collection","business_context":"We are an artisanal e-commerce shop delivering worldwide. Shipping is free within India."})
});
const data = await response.json();
console.log(data);
import requests
url = "https://test.rsflowhub.com/api/v1/ai/comment-analyze"
headers = {
"Content-Type": "application/json",
"X-API-Key": "rfh_your_api_key"
}
payload = {"comment":"Do you ship to Mumbai? I would love to order this today!","platform":"Instagram","post_context":"Photo of our new handcrafted leather boots collection","business_context":"We are an artisanal e-commerce shop delivering worldwide. Shipping is free within India."}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Detects positive, negative, neutral, and emotional sub-tones such as excitement, curiosity, or frustration.
Automatically flags whether a comment requires an explicit business reply (e.g., shipping inquiries vs casual emojis).
Categorizes comments into pricing, shipping, product feedback, technical support, or general praise.
Contextualized analysis tailored for Instagram, TikTok, YouTube, LinkedIn, and X comment structures.
See how developers integrate this API endpoint into production apps.
Automatically prioritize buyer questions over generic emojis in multi-brand social media inboxes.
Analyze thousands of viewer comments on sponsored YouTube videos and Instagram reels to measure campaign ROI.
Flag urgent customer complaints posted on social channels for instant support ticket creation.
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 2 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 |
comment
string
Raw social media comment text payload to analyze.
platform
string
Target social platform ("Instagram", "YouTube", "TikTok", "Twitter", "LinkedIn").
post_context
string
Contextual description or caption of the original social media post.
business_context
string
Business background rules or product context.
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": "positive",
"intent": "purchase_question",
"language": "en",
"topics": [
"pricing",
"availability"
],
"requires_reply": true,
"priority": "high",
"recommended_action": "Reply with a link to the pricing page.",
"confidence": 0.9
}
},
"meta": {
"version": "v1",
"timestamp": "2026-10-05T09:30:00Z"
}
}
Everything you need to know about integrating and using Social Media Comment & Tone Analysis API.
Get started in minutes with free welcome credits and instant API key generation.
🐞 Found an issue? Report it and our team replies within 1 business day.
💡 Need a custom endpoint? Tell us what you need and we will scope it with you.