Overview
The News Analytics API is a specialized interface designed to deliver rich News Analytics data for over 250,000 companies in our database. It synthesizes a wealth of news-related information and provides it to users in an easily accessible, digestible format.
Utilizing the News Analytics API, users can unlock insights from a broad array of news analytics data. This encompasses sentiment analysis, news volume, various scoring metrics, and more. By tapping into these resources, users can gain a nuanced understanding of the narrative surrounding a company.
The sentiment analysis feature offers valuable insights into the overall tone of news articles associated with the company. It's a powerful tool for users to gauge public sentiment and market perception, which can inform decision-making processes.
News volume data surfaced by the News Analytics API gives users the ability to track the amount of news coverage a company receives over time. It's an effective way to measure visibility and awareness, and can provide critical context for other analytics.
The API also delivers various scores, which can encompass a range of metrics such as popularity, impact, or relevance scores, providing a quantified perspective on a company's news presence.
Detailed information about each of these capabilities, including the associated endpoints, parameters, and response formats will be provided in the upcoming sections.
List of Endpoints
All URIs are relative to https://data.api.yukkalab.com/9-3-5/2023-12-13-0
| Method | HTTP request | Description |
|---|---|---|
| Sentiment | GET /api/{entity_type}/{alpha_id}/quotient_ts | Get Sentiment for a company |
| Sentiment batched | POST /v2/api/portfolio/quotient_ts_itemized | Get Sentiment for many companies |
| News Volume | GET /api/{entity_type}/{alpha_id}/volume_ts | Get News Volume for a company |
| News Volume batched | POST /v2/api/portfolio/volume_ts_itemized | Get News Volume for many companies |
| Events | GET /v2/api/{entity_type}/{alpha_id}/events_ts | Get Events for a company |
| Events batched | POST /v2/api/portfolio/events_ts_itemized | Get Events for many companies |
| Scores | GET /api/{entity_type}/{alpha_id}/score_ts | Get Scores for a company |
| Scores batched | POST /v2/api/portfolio/score_ts_itemized | Get Scores for many companies |
Batched Data Export
Our API supports batched data exports for scenarios requiring data across multiple companies. These batched endpoints are designed to handle requests for data spanning numerous companies, offering an efficient way to pull aggregated data. Due to the volume of data involved in these requests, they are made as POST requests.
Understanding the Batched Endpoints
The batched endpoints, referred to as _itemized endpoints, allow for the export of data across multiple companies in a single request. This functionality is critical for users who need to aggregate or compare data across a range of companies.
Limitations
To ensure optimal performance and manageability of data, there is a cap on the number of companies that can be included in a single batched request. The maximum number of companies permitted per batch is 100. This limit is set to prevent excessive load times and ensure data accuracy and integrity.
Calculating Batch Sizes
When pulling data for an extended period or for numerous companies, it's essential to calculate the number of companies to include in each batch. A formula has been devised to help determine the optimal batch size, balancing the need for comprehensive data against the constraints of data processing and transmission:
#companies = max(10, 140 - (10 * time(in years)))
Examples:
- Scenario 1: Requesting data spanning from 2007 to 2024 (a 17-year span) results in a calculation of:
#companies = max(10, 140 - (10 * 17)) => #companies = 10
In this case, you would group your data requests into batches of 10 companies each.
- Scenario 2: For data requests from 2020 to 2024 (a 4-year span):
- #companies = max(10, 140 - (10 * 4)) => #companies = 100
This allows for a single batch to include up to 100 companies, maximizing efficiency within the constraints.
This calculated batch size helps in planning data exports, especially when dealing with extensive datasets over long periods. By adjusting the number of companies per batch based on the duration of data required, users can optimize their data retrieval processes, ensuring they receive the necessary data without overwhelming the system or causing excessive processing times.
Implementation
For implementation, when the data needed spans across numerous years for multiple companies, it's advisable to segment the requests into batches as calculated using the formula above. Loop through these batches to retrieve data for the set number of companies at a time, ensuring efficient data processing and adherence to the API's usage guidelines.