Market Research with Entity Extraction

Extract companies, people, and trends from market research documents. Automate competitive intelligence with entity extraction.

The Problem

Market researchers manually read through hundreds of articles, reports, and filings to track competitors, key players, and industry trends. This process is slow and makes it easy to miss important mentions.

The Solution

Automatically extract companies, executives, products, and locations from news articles, analyst reports, and SEC filings. Build comprehensive databases of market intelligence with structured entity data.

Key Benefits

Track competitor mentions across news sources
Identify key executives and leadership changes
Monitor geographic expansion and market moves
Build company relationship maps from mentions
Analyze industry trends through entity patterns
Create automated competitive intelligence feeds

Code Example

python

import requests
from collections import Counter

def analyze_market_news(articles):
    """Extract market intelligence from news articles"""
    all_companies = []
    all_people = []
    all_locations = []

    for article in articles:
        response = requests.post(
            "https://api.entity-detector.com/v1/analyze",
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            json={"text": article["content"]}
        )

        entities = response.json()["entities"]
        all_companies.extend(entities.get("organizations", []))
        all_people.extend(entities.get("persons", []))
        all_locations.extend(entities.get("locations", []))

    return {
        "top_companies": Counter(all_companies).most_common(10),
        "top_people": Counter(all_people).most_common(10),
        "top_locations": Counter(all_locations).most_common(10),
        "total_articles": len(articles)
    }

# Analyze a batch of news articles
news_batch = [{"content": "..."}, {"content": "..."}]
intelligence = analyze_market_news(news_batch)

print("Most mentioned companies:", intelligence["top_companies"])

Example Output

json

{
  "top_companies": [
    ["Apple", 45],
    ["Microsoft", 38],
    ["Google", 32],
    ["Amazon", 28],
    ["Tesla", 24]
  ],
  "top_people": [
    ["Elon Musk", 18],
    ["Tim Cook", 12],
    ["Satya Nadella", 9]
  ],
  "top_locations": [
    ["Silicon Valley", 22],
    ["New York", 15],
    ["China", 12]
  ],
  "total_articles": 150
}

Ready to get started?

Try entity extraction for your market research workflow.

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