Extracting Abnormal Text Information from Asset Data under Natural Language Processing

Abstract

Due to the large amount of information and complex content in text, efficient extraction and analysis of abnormal information in the text is currently a challenge. Therefore, this article utilized natural language processing (NLP) technology to extract and analyze abnormal text information from asset data in asset data. This article first took Chinese A-share listed companies from 2016 to 2022 as the research object, collected relevant asset data texts, and defined the main variables. Subsequently, this article utilized the idea of NLP to construct a corresponding model for extracting and analyzing abnormal text information from asset data for experimental testing. This article conducted experimental tests on five types of abnormal text in asset data, including revenue decline, profit decline, stock price volatility increase, debt ratio increase, and legal litigation risk, to verify the detection ability of NLP technology. The results showed that when faced with these five abnormalities, the detection rate was higher than 0.9. Experimental results have shown that NLP algorithms can effectively extract and analyze text anomalies in asset data, thereby improving the accuracy and credibility of asset data.