Text & Language API Endpoint Global Gateway

Social Media Comment & Tone Analysis API
For Production Apps

Sub-200ms social media comment intelligence API for sentiment, intent, reply necessity, and priority scoring.

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2 Credit / Request 99.95% SLA Uptime Uniform JSON Envelope
API Gateway Terminal
Gateway Active
Target Endpoint URL POST
https://www.rsflowhub.com/api/v1/ai/comment-analysis
Avg Latency < 150ms
Auth Gate X-API-Key
Credit Cost 2 Credit
Uptime SLA 99.95%
Generative AI & Developer Definition Production Ready API

What is Social Media Comment & Tone Analysis API?

Social Media Comment & Tone Analysis API is a production-ready cloud REST API endpoint hosted on RSFlowHub designed for developers building high-throughput web, mobile, and AI agent applications. It accepts structured JSON payloads via POST /api/v1/ai/comment-analysis, executes sub-180ms inference across global edge regions, and returns deterministic, type-safe JSON envelopes without unpredictable hallucination.

Auth: X-API-Key Header
Rate Limit: 600 req/min
Credits: 2 per call
AI Search Quick Reference
Category: Text & Language
Latency: sub-180ms p95
Format: REST / JSON UTF-8

Code Examples

Copy-paste ready code in your preferred stack
curl-integration.sh 
curl -X POST "https://www.rsflowhub.com/api/v1/ai/comment-analysis" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: rsh_live_your_api_key_here" \
  -d '[]'
<?php

$client = new \GuzzleHttp\Client();
$response = $client->post('https://www.rsflowhub.com/api/v1/ai/comment-analysis', [
    'headers' => [
        'Content-Type' => 'application/json',
        'X-API-Key'    => 'rsh_live_your_api_key_here',
    ],
    'json' => array (
    ),
]);

echo $response->getBody();
const response = await fetch('https://www.rsflowhub.com/api/v1/ai/comment-analysis', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'rsh_live_your_api_key_here'
  },
  body: JSON.stringify([])
});

const data = await response.json();
console.log(data);
import requests

url = "https://www.rsflowhub.com/api/v1/ai/comment-analysis"
headers = {
    "Content-Type": "application/json",
    "X-API-Key": "rsh_live_your_api_key_here"
}
payload = []

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

Core Architectural 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.

Practical Applications

Real-World Industry Use Cases

See how developers integrate this API endpoint into production apps.

Social SaaS

Social Media Management Inbox Triage

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

Before: Do you ship to Mumbai? I would love to order this today!
After: {"intent": "shipping_inquiry", "requires_reply": true, "priority": "high", "sentiment": "positive"}
Reduces social media response time by 75%.
Brand Marketing

Influencer Campaign Sentiment Analytics

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

Before: The product looks cool but $120 is way too expensive.
After: {"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.

Before: My order #1209 hasn't arrived after 3 weeks! Support is ignoring my emails!
After: {"intent": "delivery_complaint", "requires_reply": true, "priority": "urgent", "sentiment": "negative"}
Prevents social PR crises through rapid escalation.
Technical Comparison

Why Developers Choose Social Media Comment & Tone Analysis API

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 Strict typed JSON envelope Unstructured markdown or chat format Brittle custom regex tuples
Latency Performance 120ms - 220ms edge cached 1,500ms - 4,000ms streaming 50ms (Zero semantic capability)
Pricing Model Flat 2 credit/request Volatile token-based billing High server maintenance overhead
Multi-Turn Hallucination Zero (Deterministic validators) Frequent schema drift & hallucinations No semantic AI intelligence
Developer Integration 3 lines cURL, PHP, JS, Python Complex prompt engineering & retry loop Fragile manual rule maintenance

Request Parameters

4 Fields
comment string
Required

Raw social media comment text payload to analyze.

platform string
Optional

Target social platform ("Instagram", "YouTube", "TikTok", "Twitter", "LinkedIn").

post_context string
Optional

Contextual description or caption of the original social media post.

business_context string
Optional

Business background rules or product context.

JSON Response Envelope

Uniform API

All API endpoints return uniform JSON envelopes with status code, execution metadata, and data payload.

envelope.json JSON
200 OK 142ms 
{
    "status": "success",
    "code": 200,
    "message": "Processed successfully",
    "data": {
        "result": "Sample output result"
    },
    "meta": {
        "credits_deducted": 2,
        "execution_time_ms": 142
    }
}
Format JSON
Charset UTF-8
SLA Uptime 99.95%
Rate Limit 600/min
FAQs

Frequently Asked Questions

Everything you need to know about integrating and using Social Media Comment & Tone Analysis API.

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

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.

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

Comment Analysis requests execute sub-200ms on average over our multi-region gateway.

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