Optimizing Transformer based on high-performance optimizer for predicting employment sentiment in American social media content

TL;DR

This paper enhances a Transformer model with swarm intelligence optimization to accurately predict employment sentiment from American social media content, demonstrating significant improvements in accuracy and generalization.

Contribution

It introduces a novel combination of Transformer and swarm intelligence optimization for social media sentiment analysis, achieving higher accuracy and better generalization than previous methods.

Findings

Model accuracy increased from 49.27% to 82.83%.

Test set accuracy reached 82.91%, with only 3.24% difference from training.

Model showed strong performance in classification metrics like AUC and F-measure.

Abstract

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.