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ESG News Sentiment Dataset - Trial Product
Provided By: Amenity Analytics
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ESG News Sentiment Dataset - Trial Product
Provided By: Amenity Analytics
This trial dataset is industrial-scale NLP applied to thousands of news sources to develop in-depth, real-time scoring at the company level on ESG issues. It provides company-specific scoring on 12,000 companies globally to track portfolio and company exposures. The score is the result of the net sentiment divided by the total negative and positive extractions in the previous 3 days, and per ESG topic. Also includes counts making up those scores. *The trial dataset does not update.*
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Payment schedule: Upfront payment | Offer auto-renewal: Supported
$0 for 1 month
Overview
This dataset can be treated as a stand alone data product that could be integrated into any big data analytics tool at the security level. Alternatively, for customers wishing to procure Amenity Analytics’ custom dashboard can do so via Marketplace.
ESG News Sentiment Dataset - Trial
Amenity Analytics has created an ESG dataset that provides company and sector-level analysis of hundreds of news sources for monitoring and tracking ESG issues in-depth.
The entities covered in this dataset are a majority of public companies worldwide ( 12,000 companies). The underlying source of the data is top 100-200 English news sources from LexisNexis.
Amenity applies natural language processing and sentiment analysis on news to derive a numerical score (signal). The score is the result of the net sentiment divided by the total negative and positive extractions of the previous 3 calendar days, and per ESG topic. Also includes counts that make up those scores.
The Values in this dataset are represented with scores between -1 and 1. A score of -1 is the most negative and +1 is the most positive. Neutral is 0.
The dataset includes total score + counts and score + counts per key driver (Governance, General, Social and Environmental).
Key Benefits
- Systematically evaluate and quantify the materiality of Environmental, Social and Governance (ESG) factors in the news on companies in your investment universe
- Unbiased and transparent data. Our ESG model employs contextual analysis and language patterns to capture and analyze all critical events across a rich dataset of thousands of news sources providing investors broadly aggregated and unbiased E, S, and G-related evidence and scores
- Accuracy at scale. Analyze and monitor developments related to your ESG themes across your investment universe of watchlists, portfolios, and individual equities
- Via Marketplace: Dashboard interface. Features an intuitive user interface that applies an ESG lens to companies within your investment universe. Create sentiment summaries, or segment baskets of equities based on distinct ESG profiles
Use Cases
- Screening and Idea Generation
- Stock Selection and Relative Stock Selection
- News Surveillance and Monitoring
- Factor Attribution
- Baskets and Trading Structure Creation
- Alpha Generation via Analysis
Verticals
- Asset Managers
- Banks
- Capital Markets
- Insurance
Dataset General Information
Description | Value |
---|---|
Update Frequency | Daily |
Data Source(s) | LexisNexis |
Original Publisher of data | Various News Sources |
Time period coverage | 2018-01-01 till present |
Is historical data “point-in-time” | Yes |
Data Set(s) Format(s) | CSV |
Raw or scraped data | Raw |
Number of companies/brands covered | All US public companies and most public companies throughout the world. |
Standard entity identifiers | ticker + region, exchange |
Data Description and Data Dictionary
The file contains the the following fields:
Column Name | Description | Data Type | Example |
---|---|---|---|
companyId | FactSet's unique identifier for a company. Helps in tracking a company in instances where the company’s ticker has changed | string | "000D63-E" |
companyName | The company name | string | "Apple" |
ticker | The company's ticker symbol | string | "AAPL" |
region | The region of the company’s ticker | string | “US” |
exchange | The exchange the ticker is in | string | “NYSE” |
date | A date for the aggregate ESG scores of a company | date | "2021-01-01" |
totalPositiveCount | Total count of positive extractions for a date | integer | "3" |
totalNegativeCount | Total count of negative extractions for a date | integer | "5" |
totalScoreUnweighted | An unweighted sentiment score for a company. Sentiment score defined as (total positive extractions - total negative extractions) / (total positive extractions + total negative extractions -- of the last 3 days) | float | "-0.676754" |
totalWeightedCountPositive | Total weighted count of positive extractions for a date | integer | "12" |
totalWeightedCountNegative | Total weighted count of negative extractions for a date | integer | "10" |
totalScoreWeighted | A weighted sentiment score for a company. Sentiment score defined as (total weighted Positive extraction - total weighted negative extractions) / (total weighted positive extractions + total weighted negative extractions -- of the last 3 days) | float | "-0.6" |
generalCountPositive | Total count of positive extractions related to General key driver, for a date | integer | "5" |
generalCountNegative | Total count of negative extractions related to General key driver, for a date | integer | "5" |
generalScoreUnweighted | An unweighted sentiment score for a company for extraction related to XXXX. Sentiment score defined as (total unweighted positive extractions - total unweighted negative extractions) / (total unweighted positive extractions + total unweighted negative extractions -- of the last 3 days) | float | "-1.0" |
generalWeightedCountPositive | Total weighted count of positive extractions related to General key driver, for a date | integer | "5" |
generalWeightedCountNegative | Total weighted count of negative extractions related to General key driver, for a date | integer | "5" |
generalScoreWeighted | A weighted sentiment score for a company for extraction related to General key driver. Sentiment score defined as (total weighted Positive extraction - total weighted negative extractions) / (total weighted positive extractions + total weighted negative extractions --of the last 3 days) | float | "-1.0" |
governanceCountPositive | Total count of positive extractions related to Governance key driver, for a date | integer | "5" |
governanceCountNegative | Total count of negative extractions related to Governance key driver, for a date | integer | "5" |
governanceScoreUnweighted | An unweighted sentiment score for a company for extraction related to Governance key driver. Sentiment score defined as (total unweighted positive extractions - total unweighted negative extractions) / (total unweighted positive extractions + total unweighted negative extractions of the last 3 days) | float | "-1.0" |
governanceWeightedCountPositive | Total weighted count of positive extractions related to Governance key driver, for a date | integer | "5" |
governanceWeightedCountNegative | Total weighted count of negative extractions related to Governance key driver, for a date | integer | "5" |
governanceScoreWeighted | A weighted sentiment score for a company for extraction related to Governance key driver. Sentiment score defined as (total weighted positive extraction - total weighted negative extractions) / (total weighted positive extractions + total weighted negative extractions of the last 3 days) | float | "-1.0" |
environmentalCountPositive | Total count of positive extractions related to Environmental key driver, for a date | integer | "5" |
environmentalCountNegative | Total count of negative extractions related to Environmental key driver, for a date | integer | "5" |
environmentalScoreUnweighted | An unweighted sentiment score for a company for extraction related to Environmental key driver. Sentiment score defined as (total unweighted positive extractions - total unweighted negative extractions) / (total unweighted positive extractions + total unweighted negative extractions -- of the last 3 days) | float | "-1.0" |
environmentalWeightedCountPositive | Total weighted count of positive extractions related to Environmental key driver, for a date | integer | "5" |
environmentalWeightedCountNegative | Total weighted count of negative extractions related to Environmental key driver, for a date | integer | "5" |
environmentalScoreWeighted | A weighted sentiment score for a company for extraction related to Environmental key driver. Sentiment score defined as (total weighted positive extraction - total weighted negative extractions) / (total weighted positive extractions + total weighted negative extractions -- of the last 3 days) | float | "-1.0" |
socialCountPositive | Total count of positive extractions related to Social key driver, for a date | integer | "5" |
socialCountNegative | Total count of negative extractions related to Social key driver, for a date | integer | "5" |
socialScoreUnweighted | An unweighted sentiment score for a company for extraction related to Social key driver. Sentiment score defined as (total unweighted positive extraction - total unweighted negative extractions) / (total unweighted positive extractions + total unweighted negative extractions --of hte last 3 days) | float | "-1.0" |
socialWeightedCountPositive | Total weighted count of positive extractions related to Social key driver, for a date | integer | "5" |
socialWeightedCountNegative | Total weighted count of negative extractions related to Social key driver, for a date | integer | "5" |
socialScoreWeighted | A weighted sentiment score for a company for extraction related to Social key driver. Sentiment score defined as (total weighted positive extraction - total weighted negative extractions) / (total weighted positive extractions + total weighted negative extractions -- of the last 3 days) | float | "-1.0" |
Update Frequency
- Paid subscriber data updates daily
- Trial data updates infrequently
Applications
- What you can do: Develop time series, use for backtesting or fundamental analysis or for company rankings
- What you cannot do: Dive into specific event/extractions counts or see what the text extractions were; in addition this cannot be resold in part or in full as a commercial dataset
Additional Information
- API Documentation Available to subscribed users only
Regulatory and Compliance Information
Portions of the Services (including the Content) may be provided through third-party providers, such as FactSet, LexisNexis, and/or EDGAR, which may impose certain restrictions or additional terms and conditions.
Required Information to Start a Subscription
- EIN number
- Number of applications,
- Number of users
- Number of regions
- Number companies in coverage
- User(s) email addresses
Need Help?
- If you have questions about our products, contact us using the support information: vijay@amenityanalytics.com
About Amenity
We develop cloud-based analytics solutions to help businesses draw actionable insights from text on a massive scale. Fortune 100 companies, hedge funds, financial exchanges, and insurance companies rely on our proprietary NLP technology for use on sources ranging from regulatory filings and earnings call transcripts to news coverage, social media activity, and research reports.
Update Frequency
- Paid subscriber data updates daily
- Trial data updates infrequently
Applications
- What you can do: Develop time series, use for backtesting or fundamental analysis or for company rankings
- What you cannot do: Dive into specific event/extractions counts or see what the text extractions were; in addition this cannot be resold in part or in full as a commercial dataset
Regulatory and Compliance Information
Portions of the Services (including the Content) may be provided through third-party providers, such as FactSet, LexisNexis, and/or EDGAR, which may impose certain restrictions or additional terms and conditions.
Required Information to Start a Subscription
- EIN number
- Number of applications
- Number of users
- Number of regions
- Number companies in coverage
- User(s) email addresses
Need Help?
- If you have questions about our products, contact us using the support information: vijay@amenityanalytics.com
About Amenity
We develop cloud-based analytics solutions to help businesses draw actionable insights from text on a massive scale. Fortune 100 companies, hedge funds, financial exchanges, and insurance companies rely on our proprietary NLP technology for use on sources ranging from regulatory filings and earnings call transcripts to news coverage, social media activity, and research reports.
Provided By
Fulfillment Method
AWS Data Exchange
Data sets (1)
You will receive access to the following data sets
Revision access rules
All historical revisions | All future revisions
Name | Type | Data dictionary | AWS Region |
---|---|---|---|
ESG News Sentiment Dataset - Trial | Not included | US East (N. Virginia) |
Usage information
By subscribing to this product, you agree that your use of this product is subject to the provider's offer terms including pricing information and Data Subscription Agreement . Your use of AWS services remains subject to the AWS Customer Agreement or other agreement with AWS governing your use of such services.
Support information
Support contact email address
Support contact URL
Refund policy
Not applicable for trials.
General AWS Data Exchange support