Content Analysis API Fresh

Content Analysis API helps you discover citations of a target keyword or brand across the web and analyze the sentiment around those mentions. It powers brand monitoring, reputation management, and competitive content research at scale.

The API identifies positive, negative, and neutral polarity in any text and detects the following sentiment connotations:

• Anger
• Happiness
• Love
• Sadness
• Share (desire to share the content)
• Fun

Method: Live only — all endpoints return instant results without a task queue.

Rate limit: Up to 2,000 API calls per minute.

All Endpoints (6 total)

Endpoint	Method	Description	Cost Est.
/v3/content_analysis/search/live	POST	All citations for a keyword with full metadata per citation	~$0.005
/v3/content_analysis/summary/live	POST	Aggregate citation stats — total count, domain breakdown, avg sentiment	~$0.002
/v3/content_analysis/sentiment_analysis/live	POST	Citation counts broken down by polarity and connotation	~$0.002
/v3/content_analysis/rating_distribution/live	POST	Citation distribution by content safety rating	~$0.002
/v3/content_analysis/phrase_trends/live	POST	Citation volume and sentiment by date — build trend charts	~$0.002
/v3/content_analysis/category_trends/live	POST	Citation trends by date within a specific content category	~$0.002

Available Endpoints (Detail)

Endpoint	What it returns
Search	All citations for a target keyword with full detail on each citation
Summary	Complete overview of citation data for the target keyword
Sentiment Analysis	Citation breakdown by positive/negative/neutral polarity and sentiment connotations
Rating Distribution	Citation stats distribution by content rating
Phrase Trends	Citation volume and sentiment trends by date for a target keyword
Category Trends	Citation trends by date within a target category

Search Endpoint

POST https://api.dataforseo.com/v3/content_analysis/search/live

Returns all citations for a keyword with full metadata per citation. Supports filtering and sorting — filtering does not incur extra charges.

Use this for:

• Finding every page on the web that mentions your brand
• Pulling the sentiment classification for each citation
• Identifying the domains citing your competitors

cURL Example

curl --request POST \
  --url https://api.dataforseo.com/v3/content_analysis/search/live \
  --header 'Authorization: Basic BASE64(login:password)' \
  --header 'Content-Type: application/json' \
  --data '[
    {
      "keyword": "your brand name",
      "limit": 50,
      "filters": [
        ["sentiment_connotations.positive", ">", 0]
      ]
    }
  ]'

Summary Endpoint

POST https://api.dataforseo.com/v3/content_analysis/summary/live

Returns aggregate statistics for all citations of a keyword — total count, domain breakdown, average sentiment scores, and content rating distribution. Use this as the first call to get a broad picture before diving into individual citations.

curl --request POST \
  --url https://api.dataforseo.com/v3/content_analysis/summary/live \
  --header 'Authorization: Basic BASE64(login:password)' \
  --header 'Content-Type: application/json' \
  --data '[{"keyword": "your brand name"}]'

Sentiment Analysis Endpoint

POST https://api.dataforseo.com/v3/content_analysis/sentiment_analysis/live

Returns granular citation counts broken down by:

• Polarity: positive, negative, neutral percentages
• Connotation: anger, happiness, love, sadness, share, fun

Use this to track reputation shifts — for example, detecting a spike in negative sentiment after a product issue or policy change.

curl --request POST \
  --url https://api.dataforseo.com/v3/content_analysis/sentiment_analysis/live \
  --header 'Authorization: Basic BASE64(login:password)' \
  --header 'Content-Type: application/json' \
  --data '[{"keyword": "your brand name"}]'

Phrase Trends Endpoint

POST https://api.dataforseo.com/v3/content_analysis/phrase_trends/live

Returns citation volume and sentiment data by date for a target keyword. Use this to build time-series charts showing how mention volume and sentiment have changed over time.

Category Trends Endpoint

POST https://api.dataforseo.com/v3/content_analysis/category_trends/live

Returns citation trend data for a keyword within a specific content category. Useful for understanding how mentions in a particular topic area (e.g., "technology news") have shifted over time.

Filtering

The Search endpoint supports custom filters. You can filter on:

• Sentiment polarity values
• Connotation scores
• Domain or URL patterns
• Content rating
• Date ranges

Filters are free — applying them does not increase the cost per request.

Python Example — Brand Monitoring Pipeline

import requests
from requests.auth import HTTPBasicAuth
import json

LOGIN = "your_login"
PASSWORD = "your_password"

def content_analysis_request(endpoint, payload):
    url = f"https://api.dataforseo.com/v3/content_analysis/{endpoint}/live"
    r = requests.post(
        url,
        auth=HTTPBasicAuth(LOGIN, PASSWORD),
        headers={"Content-Type": "application/json"},
        data=json.dumps(payload)
    )
    return r.json()


def brand_sentiment_report(brand_name):
    # 1. Get summary stats
    summary = content_analysis_request("summary", [{"keyword": brand_name}])
    summary_data = summary["tasks"][0]["result"][0]

    # 2. Get sentiment breakdown
    sentiment = content_analysis_request("sentiment_analysis", [{"keyword": brand_name}])
    sentiment_data = sentiment["tasks"][0]["result"][0]

    # 3. Get phrase trends
    trends = content_analysis_request("phrase_trends", [{"keyword": brand_name}])
    trends_data = trends["tasks"][0]["result"][0]

    print(f"\n=== Brand Report: {brand_name} ===")
    print(f"Total citations: {summary_data.get('total_count', 'N/A')}")
    print(f"\nSentiment breakdown:")
    print(json.dumps(sentiment_data, indent=2))
    print(f"\nMention trend (last 12 months):")
    print(json.dumps(trends_data, indent=2))


def find_negative_mentions(brand_name, limit=20):
    payload = [{
        "keyword": brand_name,
        "limit": limit,
        "filters": [["sentiment_connotations.anger", ">", 0.5]],
        "order_by": ["sentiment_connotations.anger,desc"]
    }]

    results = content_analysis_request("search", payload)
    items = results["tasks"][0]["result"][0]["items"]

    print(f"\nTop {len(items)} negative mentions of '{brand_name}':")
    for item in items:
        print(f"  - {item.get('url')} | Anger: {item.get('sentiment_connotations', {}).get('anger', 0):.2f}")


# Usage
brand_sentiment_report("your company name")
find_negative_mentions("your company name")

Response Fields (Search Endpoint)

Field	Type	Description
total_count	integer	Total citations found
items_count	integer	Citations returned in response
items[].url	string	URL of the citing page
items[].domain	string	Domain of the citing page
items[].title	string	Page title
items[].snippet	string	Relevant excerpt containing the keyword
items[].sentiment_polarity	string	positive, negative, or neutral
items[].sentiment_connotations	object	Scores for anger, happiness, love, sadness, share, fun
items[].content_rating	string	Content safety rating
items[].date_published	string	Publication date

Notes

• All Content Analysis endpoints use the Live method only.
• The Search endpoint's filtering capability does not add to cost — apply as many filters as needed.
• Sentiment classification is automated and based on DataForSEO's NLP models. It performs best on English-language content.
• For multi-language brand monitoring, filter results by language_code to separate analysis by market.