ML Analyze

The ML Analyze endpoint lets you run AI-powered analysis on any text, independent of social media fetching.

Endpoint Overview

Endpoint	Description	Credits
`POST /ml/analyze`	Analyze text with ML models	0.5-1 per text per analysis

Analyze Text

Run sentiment, topic, intent, and keyword analysis on any text content.

Basic Request

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/ml/analyze" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": ["This product is amazing! Best purchase ever."],
    "enrich": ["sentiment"]
  }'

All Analysis Types

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/ml/analyze" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": [
      "This product is amazing! Best purchase ever.",
      "Terrible customer service, waited 2 hours on hold.",
      "How do I return this item?"
    ],
    "enrich": ["sentiment", "topics", "intent", "keywords"],
    "categories": {
      "topic": ["product", "service", "shipping", "returns", "pricing"]
    }
  }'

Parameters

Parameter	Type	Required	Default	Description
`texts`	array	Yes	-	List of texts to analyze (1-100)
`enrich`	array	Yes	-	Analysis types to run
`categories`	object	No	-	Custom topic categories
`llm_provider`	string	No	gemini-2.0-flash	LLM provider for analysis

Available Enrichments

Type	Description	Cost
`sentiment`	Positive, negative, or neutral	0.5 credits/text
`topics`	Extract main topics	1 credit/text
`intent`	Identify user intent	1 credit/text
`keywords`	Extract key phrases	0.5 credits/text

Response

{
  "success": true,
  "data": {
    "results": [
      {
        "text": "This product is amazing! Best purchase ever.",
        "enrichment": {
          "sentiment": "positive",
          "sentiment_score": 0.95,
          "topics": ["product"],
          "intent": "praise",
          "keywords": ["product", "amazing", "best", "purchase"]
        }
      },
      {
        "text": "Terrible customer service, waited 2 hours on hold.",
        "enrichment": {
          "sentiment": "negative",
          "sentiment_score": 0.12,
          "topics": ["service"],
          "intent": "complaint",
          "keywords": ["customer service", "waited", "hours", "hold"]
        }
      },
      {
        "text": "How do I return this item?",
        "enrichment": {
          "sentiment": "neutral",
          "sentiment_score": 0.50,
          "topics": ["returns"],
          "intent": "question",
          "keywords": ["return", "item"]
        }
      }
    ]
  },
  "metadata": {
    "credits_used": 9,
    "processing_time": 1.234,
    "enrichments": ["sentiment", "topics", "intent", "keywords"]
  }
}

Custom Categories

Define your own topic categories for more relevant classification:

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/ml/analyze" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": ["The new iPhone camera is incredible for low-light photos"],
    "enrich": ["topics"],
    "categories": {
      "topic": ["camera", "battery", "display", "performance", "design", "price"]
    }
  }'

Use Cases

Customer Feedback

Analyze support tickets, reviews, and survey responses

Content Moderation

Detect sentiment and intent in user-generated content

Market Research

Extract topics and sentiment from competitor mentions

Social Listening

Process imported data from any source

Python Examples

Batch Analysis

import requests

def analyze_texts(texts, analyses=["sentiment", "topics"]):
    response = requests.post(
        "https://data-api.deepdiveplatform.com/api/v1/ml/analyze",
        headers={"X-API-Key": "your-api-key"},
        json={
            "texts": texts,
            "enrich": analyses
        }
    )
    return response.json()

# Analyze customer feedback
feedback = [
    "Love this product!",
    "Shipping took forever",
    "Great value for money",
    "How do I get a refund?"
]

results = analyze_texts(feedback, ["sentiment", "intent"])

for result in results["data"]["results"]:
    print(f"{result['enrichment']['sentiment']}: {result['text'][:50]}...")

Sentiment Distribution

from collections import Counter

def get_sentiment_distribution(texts):
    results = analyze_texts(texts, ["sentiment"])

    sentiments = Counter(
        r["enrichment"]["sentiment"]
        for r in results["data"]["results"]
    )

    total = len(texts)
    return {
        "positive": sentiments["positive"] / total * 100,
        "negative": sentiments["negative"] / total * 100,
        "neutral": sentiments["neutral"] / total * 100
    }

Credit Calculation

Credits are calculated per text, per analysis type:

Texts	Analyses	Calculation	Total
10	sentiment	10 × 0.5	5 credits
10	sentiment, topics	10 × (0.5 + 1)	15 credits
10	all 4 types	10 × (0.5 + 1 + 1 + 0.5)	30 credits

Introduction

Getting Started

Core Concepts

Platform Guides

Coming Soon

Endpoint Overview

Analyze Text

Basic Request

All Analysis Types

Parameters

Available Enrichments

Response

Custom Categories

Use Cases

Customer Feedback

Content Moderation

Market Research

Social Listening

Python Examples

Batch Analysis

Sentiment Distribution

Credit Calculation

​Endpoint Overview

​Analyze Text

​Basic Request

​All Analysis Types

​Parameters

​Available Enrichments

​Response

​Custom Categories

​Use Cases

Customer Feedback

Content Moderation

Market Research

Social Listening

​Python Examples

​Batch Analysis

​Sentiment Distribution

​Credit Calculation

Endpoint Overview

Analyze Text

Basic Request

All Analysis Types

Parameters

Available Enrichments

Response

Custom Categories

Use Cases

Python Examples

Batch Analysis

Sentiment Distribution

Credit Calculation