Aspect-Based Sentiment Analysis
Analyze sentiment for specific features or subjects (e.g. "pricing", "speed", "support") within a single text block.
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Sub-200ms aspect-based sentiment classification and emotion intensity scoring across 50+ languages.
https://www.rsflowhub.com/api/v1/ai/sentiment-analysis
Real-Time Multilingual Sentiment 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/sentiment-analysis, executes sub-180ms inference across global edge regions, and returns deterministic, type-safe JSON envelopes without unpredictable hallucination.
X-API-Key Header
curl -X POST "https://www.rsflowhub.com/api/v1/ai/sentiment-analysis" \
-H "Content-Type: application/json" \
-H "X-API-Key: rsh_live_your_api_key_here" \
-d '{"text":"The dashboard is incredibly fast, but the new billing page is very confusing.","aspects":["dashboard","billing"]}'
<?php
$client = new \GuzzleHttp\Client();
$response = $client->post('https://www.rsflowhub.com/api/v1/ai/sentiment-analysis', [
'headers' => [
'Content-Type' => 'application/json',
'X-API-Key' => 'rsh_live_your_api_key_here',
],
'json' => array (
'text' => 'The dashboard is incredibly fast, but the new billing page is very confusing.',
'aspects' =>
array (
0 => 'dashboard',
1 => 'billing',
),
),
]);
echo $response->getBody();
const response = await fetch('https://www.rsflowhub.com/api/v1/ai/sentiment-analysis', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': 'rsh_live_your_api_key_here'
},
body: JSON.stringify({"text":"The dashboard is incredibly fast, but the new billing page is very confusing.","aspects":["dashboard","billing"]})
});
const data = await response.json();
console.log(data);
import requests
url = "https://www.rsflowhub.com/api/v1/ai/sentiment-analysis"
headers = {
"Content-Type": "application/json",
"X-API-Key": "rsh_live_your_api_key_here"
}
payload = {"text":"The dashboard is incredibly fast, but the new billing page is very confusing.","aspects":["dashboard","billing"]}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Analyze sentiment for specific features or subjects (e.g. "pricing", "speed", "support") within a single text block.
Returns confidence scores (-1.0 to +1.0) for positive, negative, neutral, and mixed emotional tones.
Multilingual sentiment classification across English, Spanish, French, German, Hindi, Gujarati, Japanese, and more.
Sub-200ms execution speed built for real-time stream monitoring and batch analytics.
See how developers integrate this API endpoint into production apps.
Categorize incoming customer reviews and Net Promoter Score (NPS) comments into positive and negative themes.
Monitor public social media posts and brand mentions during new feature or product launches.
Auto-flag angry or highly negative support tickets for priority routing to senior agents.
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 1 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 |
text
string
Text payload to analyze for sentiment and emotional tone.
aspects
array
Optional array of specific target aspects/features to analyze (e.g. ["pricing", "support", "speed"]).
All API endpoints return uniform JSON envelopes with status code, execution metadata, and data payload.
{
"status": "success",
"code": 200,
"message": "Processed successfully",
"data": {
"result": "Sample output result"
},
"meta": {
"credits_deducted": 1,
"execution_time_ms": 142
}
}
Everything you need to know about integrating and using Real-Time Multilingual Sentiment Analysis API.
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