Senti‐eXLM: Uyghur enhanced sentiment analysis model based on XLM

Abstract

Abstract In the field of public opinion analysis, sentiment analysis is an important basic research branch. Previous studies have successfully proved that the advanced transformer pre‐training model can be applied to this scenario in Uyghur and other low‐resource language scenarios. However, the majority of these studies are based on the traditional language anchor point and rely on the pre‐training model's cross‐lingual understanding ability. The Senti‐eXLM model proposed in this paper employs a method that allows for adaptively expanding the model's knowledge domain and dynamically adjusting the model for Uyghur language in order to improve the language's understanding and representation capability, thereby increasing the accuracy of text emotion analysis. Experiments on publicly available data sets demonstrate that when compared to the original model, the model's emotion classification accuracy is improved by 6.17%, the training convergence speed is increased by 27%, and the average reasoning time is increased by only 11%.