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ESG News Sentiment Dataset

Provided By: Amenity Analytics

ESG News Sentiment Dataset

Provided By: Amenity Analytics

This 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. Scoring derives from net sentiment divided by total neg. and pos. extractions in the transcript, and per ESG topic. Also includes counts making up those scores. * Paid subscriber datasets update daily.*

Product offers

The following offers are available for this product. Choose an offer to view the pricing and access duration options for the offer. Select an offer and continue to subscribe. Your subscription begins on the date that you accept the offer. Additional taxes or fees might apply.

Public offer

Payment schedule: Upfront payment | Offer auto-renewal: Supported
$10,000 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

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 in the previous 3 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

DescriptionValue
Update FrequencyDaily
Data Source(s)LexisNexis
Original Publisher of dataVarious News Sources
Time period coverage2018-01-01 till present
Is historical data “point-in-time”Yes
Data Set(s) Format(s)CSV
Raw or scraped dataRaw
Number of companies/brands coveredAll US public companies and most public companies throughout the world.
Standard entity identifiersticker + region, exchange

Data Description and Data Dictionary

The file contains the the following fields:

Column NameDescriptionData TypeExample
symbologyIdFactSet's unique identifier for a company. Helps in tracking a company in instances where the company’s ticker has changedstring"000D63-E"
companyNameThe company namestring"Apple"
tickerThe company's ticker symbolstring"AAPL"
regionThe region of the company’s tickerstring“US”
exchangeThe exchange the ticker is instring“NYSE”
isinThe exchange the ticker is instring“US0378331005”
sedolThe exchange the ticker is instring“2046251”
bloombergTickerThe company's bloomberg tickerstring“AAPL UW”
dateA date for the aggregate ESG scores of a companydate"2021-01-01"
totalPositiveCountTotal count of positive extractions for a dateinteger"3"
totalNegativeCountTotal count of negative extractions for a dateinteger"5"
totalCountDailyScoreAn 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"
totalWeightedPositiveCountTotal weighted count of positive extractions for a dateinteger"12"
totalWeightedNegativeCountTotal weighted count of negative extractions for a dateinteger"10"
totalWeightedCountDailyScoreA 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"
generalPositiveCountTotal count of positive extractions related to General key driver, for a dateinteger"5"
generalNegativeCountTotal count of negative extractions related to General key driver, for a dateinteger"5"
generalCountDailyScoreAn 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"
generalWeightedPositiveCountTotal weighted count of positive extractions related to General key driver, for a dateinteger"5"
generalWeightedNegativeCountTotal weighted count of negative extractions related to General key driver, for a dateinteger"5"
generalWeightedCountDailyScoreA 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"
governancePositiveCountTotal count of positive extractions related to Governance key driver, for a dateinteger"5"
governanceNegativeCountTotal count of negative extractions related to Governance key driver, for a dateinteger"5"
governanceCountDailyScoreAn 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"
governanceWeightedPositiveCountTotal weighted count of positive extractions related to Governance key driver, for a dateinteger"5"
governanceWeightedNegativeCountTotal weighted count of negative extractions related to Governance key driver, for a dateinteger"5"
governanceWeightedCountDailyScoreA 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"
environmentalPositiveCountTotal count of positive extractions related to Environmental key driver, for a dateinteger"5"
environmentalNegativeCountTotal count of negative extractions related to Environmental key driver, for a dateinteger"5"
environmentalCountDailyScoreAn 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"
environmentalWeightedPositiveCountTotal weighted count of positive extractions related to Environmental key driver, for a dateinteger"5"
environmentalWeightedNegativeCountTotal weighted count of negative extractions related to Environmental key driver, for a dateinteger"5"
environmentalCountDailyScoreA 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"
socialPositiveCountTotal count of positive extractions related to Social key driver, for a dateinteger"5"
socialNegativeCountTotal count of negative extractions related to Social key driver, for a dateinteger"5"
socialCountDailyScoreAn unweighted sentiment score for a company for extraction related to Social 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"
socialWeightedPositiveCountTotal weighted count of positive extractions related to Social key driver, for a dateinteger"5"
socialWeightedNegativeCountTotal weighted count of negative extractions related to Social key driver, for a dateinteger"5"
socialWeightedCountDailyScoreA 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

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


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

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.

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
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
Refund policy
Refunds are not offered for this product.
General AWS Data Exchange support