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

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Endpoint Overview

EndpointDescriptionCredits
POST /ml/analyzeAnalyze text with ML models0.5-1 per text per analysis

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Analyze Text

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

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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"]
  }'

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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"]
    }
  }'

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Parameters

ParameterTypeRequiredDefaultDescription
textsarrayYes-List of texts to analyze (1-100)
enricharrayYes-Analysis types to run
categoriesobjectNo-Custom topic categories
llm_providerstringNogemini-2.0-flashLLM provider for analysis

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Available Enrichments

TypeDescriptionCost
sentimentPositive, negative, or neutral0.5 credits/text
topicsExtract main topics1 credit/text
intentIdentify user intent1 credit/text
keywordsExtract key phrases0.5 credits/text

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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"]
  }
}

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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"]
    }
  }'

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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

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Python Examples

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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]}...")

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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
    }

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Credit Calculation

Credits are calculated per text, per analysis type:
TextsAnalysesCalculationTotal
10sentiment10 × 0.55 credits
10sentiment, topics10 × (0.5 + 1)15 credits
10all 4 types10 × (0.5 + 1 + 1 + 0.5)30 credits