Company classification using zero-shot learning

In recent years, natural language processing (NLP) has become increasingly important in a variety of business applications, including sentiment analysis, text classification, and named entity recognition. In this paper, we propose an approach for company classification using NLP and zero-shot learni...

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Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Rizinski, Maryan, Jankov, Andrej, Vignesh Sankaradas, Pinsky, Eugene, Miskovski, Igor, Trajanov, Dimitar
Format: Article
Language:English
Subjects:
Classification
Corporate learning
Data mining
Descriptions
Natural language processing
Sentiment analysis
Zero-shot learning
Online Access:Get full text
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Summary:In recent years, natural language processing (NLP) has become increasingly important in a variety of business applications, including sentiment analysis, text classification, and named entity recognition. In this paper, we propose an approach for company classification using NLP and zero-shot learning. Our method utilizes pre-trained transformer models to extract features from company descriptions, and then applies zero-shot learning to classify companies into relevant categories without the need for specific training data for each category. We evaluate our approach on a dataset obtained through the Wharton Research Data Services (WRDS), which comprises textual descriptions of publicly traded companies. We demonstrate that the approach can streamline the process of company classification, thereby reducing the time and resources required in traditional approaches such as the Global Industry Classification Standard (GICS). The results show that this method has potential for automation of company classification, making it a promising avenue for future research in this area.
ISSN:2331-8422