BERT를 활용한 미국 기업 공시에 대한 감성 분석 및 시각화

Abstract

Purpose This study quantified companies’ views on the COVID-19 pandemic with sentiment analysis of U.S. public companies’ disclosures. It aims to provide timely insights to shareholders, investors, and consumers by analyzing and visualizing sentiment changes over time as well as similarities and differences by industry.

Design/methodology/approach From more than fifty thousand Form 10-K and Form 10-Q published between 2020 and 2021, we extracted over one million texts related to the COVID-19 pandemic. Using the FinBERT language model fine-tuned in the finance domain, we conducted sentiment analysis of the texts, and we quantified and classified the data into positive, negative, and neutral. In addition, we illustrated the analysis results using various visualization techniques for easy understanding of information.

Findings The analysis results indicated that U.S. public companies’ overall sentiment changed over time as the COVID-19 pandemic progressed. Positive sentiment gradually increased, and negative sentiment tended to decrease over time, but there was no trend in neutral sentiment. When comparing sentiment by industry, the pattern of changes in the amount of positive and negative sentiment and time-series changes were similar in all industries, but differences among industries were shown in neutral sentiment.