Text classification in fair competition law violations using deep learning

Abstract

Introduction: Ensuring fair competition through manual review is a complex undertaking. This paper introduces the utilization of Long Short-Term Memory (LSTM) neural networks and TextCNN to establish a text classifier for classifying and reviewing normative documents. Methods: The experimental dataset used consists of policy measure samples provided by the antitrust division of the Guangdong Market Supervision Administration. We conduct a comparative analysis of the performance of LSTM and TextCNN classification models. Results: In three classification experiments conducted without an enhanced experimental dataset, the LSTM classifier achieved an accuracy of 95.74%, while the TextCNN classifier achieved an accuracy of 92.7% on the test set. Conversely, in three classification experiments utilizing an enhanced experimental dataset, the LSTM classifier demonstrated an accuracy of 96.36%, and the TextCNN classifier achieved an accuracy of 96.19% on the test set. Discussion: The experimental results highlight the effectiveness of LSTM and TextCNN in classifying and reviewing normative documents. The superior accuracy achieved with the enhanced experimental dataset underscores the potential of these models in real-world applications, particularly in tasks involving fair competition review.