Financial Fraud Detection in Listed Companies Using Deep Learning and Textual Emotion Analysis
DOI:
https://doi.org/10.5281/zenodo.12642108Keywords:
financial fraud identification, deep learning, text sentiment analysis, listed companiesAbstract
Financial fraud of listed companies refers to the bad faithless behaviour that improperly distorts accounting information, which hurts the company's management, economic development and social interests. At present, the existing research mainly focuses on financial digital data, while the exploration of text information and deep learning algorithms is relatively small. Therefore, this paper proposes a financial fraud identification method for listed companies based on deep learning and integrated text-emotional features. Firstly, the financial index is preprocessed, and then the Bi-LSTM model is used to extract the emotional features of the stock review text. Subsequently, a residual-cross-convolutional (RCC) parallel network structure is used to identify financial fraud. The network simultaneously uses a Residual network, Cross network, Convolutional network and long short-term memory network to extract the characteristics of financial fraud in a parallel way. It obtains the final recognition result through batch standardisation and a full connection layer.
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Copyright (c) 2024 Neha Romanenko, Kritika Sharma, Siddharth Verma
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