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Social Media Comment & Tone Analysis API

Social & Comments API

Social Media Comment & Tone Analysis API

Social media comment intelligence API for sentiment, intent, reply necessity, and priority scoring.

POST https://rsflowhub.com/api/v1/ai/comment-analyze

2 credits per call
X-API-Key header
JSON in, JSON out

Try it in the playground API reference

POST /api/v1/ai/comment-analyze

{
    "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."
}

200 OK response.json

{
    "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
    }
}

Definition

What is the Social Media Comment & Tone Analysis API?

Social Media Comment & Tone Analysis API is a REST API on RSFlowHub for 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, falls back to a backup provider if needed, and returns a structured JSON result, usually within 1 to 3 seconds.

Category: Social & Comments
Endpoint: POST /api/v1/ai/comment-analyze
Authentication: X-API-Key header
Price: 2 credits per call
Rate limit: 10 to 120 requests / minute by plan
Typical response: 1 to 3 seconds
Format: JSON, UTF-8

Request parameters

Send these fields as a JSON body. An invalid or missing field returns a 422 error that names the field.

Field	Type	Required	Description
`comment`	string	Required	The social media comment text (Max: 5,000 characters).
`language`	string	Optional	Optional comment language identifier.
`platform`	string	Optional	Optional platform name (e.g. "Instagram", "Facebook", "YouTube", "LinkedIn").
`post_context`	string	Optional	Optional post content or media brief context.
`business_context`	string	Optional	Optional business, product, or account rules context.
`author_context`	string	Optional	Optional commenter context details.

Code examples

Copy, replace rfh_your_api_key with your key and run. Prefer a client library? See the official SDKs.

request.sh

curl -X POST "https://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."}'

client.php

<?php

$client = new \GuzzleHttp\Client();
$response = $client->post('https://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();

client.js

const response = await fetch('https://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);

main.py

import requests

url = "https://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())

Response

Every RSFlowHub endpoint uses the same envelope, so one parser handles all of them. Failed calls are not charged.

response.json

{
    "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"
    }
}

error.json

{
    "success": false,
    "request_id": "req_8c1d4e2a-7f3b-4a9c",
    "error": {
        "code": "VALIDATION_ERROR",
        "message": "The given data was invalid.",
        "details": {
            "comment": [
                "This field is required."
            ]
        }
    },
    "meta": {
        "version": "v1",
        "timestamp": "2026-10-05T09:30:00Z"
    }
}

Envelope fields
Field	Type	Meaning
`success`	boolean	true when the call worked, false on any error.
`request_id`	string	Unique id for this call. Quote it when you contact support.
`data.result`	object	The result of this endpoint (see the example).
`error`	object	Only on failure: code, message and details per field.
`meta`	object	API version and server timestamp.

All error codes: Errors reference

Features

Multi-Dimensional Sentiment & Emotion

Detects positive, negative, neutral, and emotional sub-tones such as excitement, curiosity, or frustration.

Reply Necessity Classification

Automatically flags whether a comment requires an explicit business reply (e.g., shipping inquiries vs casual emojis).

Topic & Intent Tagging

Categorizes comments into pricing, shipping, product feedback, technical support, or general praise.

Cross-Platform Context Grounding

Contextualized analysis tailored for Instagram, TikTok, YouTube, LinkedIn, and X comment structures.

Use cases

How teams use the Social Media Comment & Tone Analysis API in production.

Social SaaS

Social Media Management Inbox Triage

Automatically prioritize buyer questions over generic emojis in multi-brand social media inboxes.

Input: Do you ship to Mumbai? I would love to order this today!
Output: {"intent": "shipping_inquiry", "requires_reply": true, "priority": "high", "sentiment": "positive"}

Shortens social media response time with ready-to-post reply drafts.

Brand Marketing

Influencer Campaign Sentiment Analytics

Analyze thousands of viewer comments on sponsored YouTube videos and Instagram reels to measure campaign ROI.

Input: The product looks cool but $120 is way too expensive.
Output: {"intent": "price_complaint", "sentiment": "negative", "topic": "pricing"}

Provides instant feedback on product reception.

Customer Success

Community Moderation & Support Escalation

Flag urgent customer complaints posted on social channels for instant support ticket creation.

Input: My order #1209 hasn't arrived after 3 weeks! Support is ignoring my emails!
Output: {"intent": "delivery_complaint", "requires_reply": true, "priority": "urgent", "sentiment": "negative"}

Prevents social PR crises through rapid escalation.

Social Media Comment & Tone Analysis API vs building it yourself

The same job done three ways.

Topic	RSFlowHub API	Raw LLM prompts	Rules / regex
Output	Validated JSON fields	Free text you have to parse	Fixed patterns only
Setup	One POST request, no prompts	Write and maintain prompts and parsing	Write and maintain rule lists
Provider outage	Automatic fallback to a backup model	You build retries and a second vendor	Not applicable
Pricing	2 credits per call, failed calls free	Per token, per vendor	Your own servers and upkeep
Understands context	Yes, AI model	Yes, AI model	No, keywords only

Frequently asked questions

Which social media platforms are supported?

The Comment Analysis API natively supports comment structures from Instagram, YouTube, TikTok, X (Twitter), LinkedIn, Facebook, and custom blog comments.

How does the API determine if a comment needs a reply?

The API evaluates intent markers (e.g. questions about shipping, pricing, or product availability) and sets requires_reply=true for actionable comments while skipping simple emojis.

Can I pass original post context for better accuracy?

Yes! Passing post_context provides grounding context so the AI understands ambiguous comments like "how much for this?".

How fast is the Comment Analysis API?

Comment Analysis requests usually complete in 1 to 3 seconds, depending on input length and the AI model. Identical repeat requests are served from cache and return faster.

How many credits does each request consume?

Standard Comment Analysis API calls consume 2 base credits per request.

Start using the Social Media Comment & Tone Analysis API

Free plan with 100 credits/month + 1,000 bonus credits when you sign in before 23 Nov. Test it in the playground first, no code needed.

Get a free API key Try it now