GeoScore

Overview

GeoScore offers a suite of APIs designed to provide location-specific insights by analyzing unstructured text data. The platform focuses on extracting sentiment, entities, and trends tied to geographical coordinates or defined regions. This capability is applied across various use cases, including understanding public opinion in specific urban areas, monitoring the sentiment around events at particular venues, or analyzing market perceptions within geographic boundaries.

The core of GeoScore's offering is its ability to perform Geo-Contextual Natural Language Processing (NLP). This involves not only tokenizing and understanding text, but also correlating that text with spatial data to deliver insights that are relevant to a specific locale. For example, a business might use GeoScore to assess the public mood regarding a new product launch in different cities, or a real estate firm could gauge sentiment about specific neighborhoods to inform investment decisions. The API handles the ingestion and processing of data, providing structured JSON responses that developers can integrate into their applications.

GeoScore is particularly suitable for developers and businesses that require granular, location-aware data analysis. This includes sectors such as market research, where understanding regional preferences is critical; urban planning, for assessing citizen feedback on local initiatives; and event management, for real-time monitoring of public perception. The platform aims to bridge the gap between textual data and geographical intelligence, offering a programmatic interface for obtaining insights that might otherwise require manual, labor-intensive analysis. Its design prioritizes ease of integration, with clear documentation and code examples provided for common programming languages like Python and Node.js to facilitate rapid development.

The API's capabilities extend beyond basic sentiment analysis, incorporating entity recognition and topic extraction within a geographical context. This allows users to identify not just the overall mood, but also the specific subjects or entities that are driving that sentiment in a particular area. For instance, an application could track mentions of specific businesses or landmarks within a designated radius and determine the associated sentiment. This level of detail supports more nuanced decision-making and targeted strategies compared to broad-stroke NLP solutions. The focus on geo-contextual data processing aligns with the growing demand for highly localized intelligence in an array of industries, including smart city initiatives and localized marketing campaigns.

Key features

• Location Sentiment API: Analyzes text data to determine public sentiment (positive, neutral, negative) mapped to specific geographical locations or boundaries.
• Geo-Contextual NLP: Processes natural language by integrating geographical information to provide location-aware entity extraction, topic modeling, and keyword analysis.
• Social Media Analysis: Ingests and analyzes publicly available social media content to extract location-based sentiment and trending topics.
• Real-time Event Monitoring: Monitors text streams in real-time to identify shifts in sentiment or emerging topics associated with physical events or locations.
• Geospatial Querying: Allows users to define geographical areas (e.g., radius around coordinates, specific polygons) for targeted data analysis.
• Developer-Friendly API: Provides a RESTful API with JSON responses, making it compatible with various programming environments, detailed in the GeoScore API reference.

Pricing

GeoScore offers a free tier for initial exploration and various paid plans based on API call volume. Enterprise-level needs can be addressed with custom solutions.

Pricing as of 2026-05-28. For current information, refer to the GeoScore pricing page.

Common integrations

• Mapping Platforms: Integrate with services like the Esri ArcGIS Developer platform or Google Maps Platform to visualize sentiment data on maps.
• Business Intelligence Tools: Connect with BI tools such as Tableau or Power BI to create dashboards for location intelligence.
• Data Warehouses: Export processed sentiment data to data warehouses (e.g., Amazon S3, Google Cloud Storage) for further analysis and long-term storage, as described in Google Cloud documentation.
• Social Media Monitoring Platforms: Enhance existing social media monitoring tools with geo-specific sentiment analysis capabilities.
• CRM Systems: Integrate with CRM platforms like Salesforce to add geographical sentiment insights to customer profiles or regional sales strategies.

Alternatives

• MeaningCloud: Offers various text analytics APIs, including sentiment analysis and topic extraction.
• IBM Watson Natural Language Understanding: Provides advanced NLP capabilities for sentiment, entity, keyword, and concept extraction.
• Google Cloud Natural Language API: A cloud-based service for text analysis, including sentiment, entity, and syntax analysis.

Getting started

To begin using GeoScore, developers typically sign up for an API key, which is used for authentication with all API endpoints. The quick start guide in the GeoScore documentation provides instructions and code examples.

The following Python example demonstrates a basic call to the GeoScore API to analyze sentiment for text associated with a specific location. Replace YOUR_API_KEY with your actual key and adjust the latitude and longitude as needed.

import requests
import json

API_KEY = "YOUR_API_KEY"
API_ENDPOINT = "https://api.geoscore.io/v1/sentiment/analyze"

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

payload = {
    "text": "The new park addition has received overwhelmingly positive feedback from local residents, improving community well-being.",
    "latitude": 34.0522,
    "longitude": -118.2437,
    "radius_km": 1
}

try:
    response = requests.post(API_ENDPOINT, headers=headers, data=json.dumps(payload))
    response.raise_for_status() # Raise an exception for HTTP errors
    result = response.json()
    print(json.dumps(result, indent=2))
except requests.exceptions.RequestException as e:
    print(f"API request failed: {e}")
    if response.status_code:
        print(f"Status Code: {response.status_code}")
        print(f"Response: {response.text}")

This Python script sends a POST request with text and geographical coordinates to the GeoScore sentiment analysis endpoint. The API then returns a JSON object containing the detected sentiment (e.g., positive, neutral, negative) and associated scores for the specified location. The radius_km parameter helps define the contextual area for the sentiment analysis, ensuring the results are relevant to the specified location. Authentication is handled by passing the API key in the Authorization header.

A similar request can be made using curl for quick testing or shell scripting:

curl -X POST \
  https://api.geoscore.io/v1/sentiment/analyze \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{ "text": "The local market had a great turnout and positive atmosphere this weekend.", "latitude": 34.0522, "longitude": -118.2437, "radius_km": 0.5 }'