Sentiment Analysis of Earnings Transcripts

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2024-10-01 (day precision)Source: 2024-10-01T00:00:00

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Other: Economic & FinancialSource: Other – Economic & Financial

Operational functions

Translation and transcription Derived by keyword rules; see methodology

Problem the AI is intended to solve

Measure and classify sentiment in bank earnings calls to identify emerging trends

Expected benefits

Additional insight into sentiments related to topics in bank earnings calls

System outputs

The probability that text inputs are positive, negative, or neutral and classifies them as such

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

System name(s)

General Support System

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Internal – Earning Transcripts

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported