用智能优化算法提升Transformer，精准预测美国社交媒体就业情绪。
Optimizing Transformer based on high-performance optimizer for predicting employment sentiment in American social media content
- 基于群智能优化改进Transformer模型，提升文本情感识别能力。
- 测试集准确率达82.91%，训练与测试误差仅差3.24%，泛化性强。
- 适合关注社会舆情分析、政策制定的数据科学家和决策者。
本文基于群智能优化算法改进Transformer模型，旨在预测美国社交媒体中与就业相关的文本情绪。通过文本预处理、特征提取与向量化，将文本数据转化为数值形式并用于模型训练。实验显示，训练过程中模型准确率从49.27%提升至82.83%，损失值从0.67降至0.35，表明训练集性能显著提升。训练集混淆矩阵显示准确率为86.15%。测试集混淆矩阵同样表现良好，准确率达82.91%。训练与测试集准确率差距仅为3.24%，说明模型具备强泛化能力。多项评估指标进一步验证模型有效性：分类准确率、敏感度、特异度及曲线下面积（AUC）均表现优异，卡帕系数为0.66，F-measure达0.80。该模型不仅提升了社交媒体中就业相关文本的情感识别精度，更具有重要应用价值——可实时捕捉社会动态，助力决策者关注公众关切，为改善就业环境提供数据支持。
原文摘要 · Abstract (English)
This article improves the Transformer model based on swarm intelligence optimization algorithm, aiming to predict the emotions of employment related text content on American social media. Through text preprocessing, feature extraction, and vectorization, the text data was successfully converted into numerical data and imported into the model for training. The experimental results show that during the training process, the accuracy of the model gradually increased from 49.27% to 82.83%, while the loss value decreased from 0.67 to 0.35, indicating a significant improvement in the performance of the model on the training set. According to the confusion matrix analysis of the training set, the accuracy of the training set is 86.15%. The confusion matrix of the test set also showed good performance, with an accuracy of 82.91%. The accuracy difference between the training set and the test set is only 3.24%, indicating that the model has strong generalization ability. In addition, the evaluation of polygon results shows that the model performs well in classification accuracy, sensitivity, specificity, and area under the curve (AUC), with a Kappa coefficient of 0.66 and an F-measure of 0.80, further verifying the effectiveness of the model in social media sentiment analysis. The improved model proposed in this article not only improves the accuracy of sentiment recognition in employment related texts on social media, but also has important practical significance. This social media based data analysis method can not only capture social dynamics in a timely manner, but also promote decision-makers to pay attention to public concerns and provide data support for improving employment conditions.
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