Author(s)

Chuwei Wang ¹, Zicheng Wang ¹, Xihe Wang ¹, Mingxing Wang ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Mingxing Wang

ABSTRACT

Sentiment analysis, a core NLP task, has important application value in public opinion monitoring, product evaluation, and social media analysis. Traditional methods based on manual feature extraction or shallow learning models have limitations in complex text scenarios. To address these, this paper designs and implements a sentiment analysis system based on an improved RoBERTa model. The system selects three real public datasets, optimizes data quality through text cleaning, normalization, and back - translation augmentation. It integrates an emotional attention mechanism and introduces FGSM adversarial training. Ablation and comparative experiments are conducted. Results show the proposed model achieves an average F1 - score of 94.2% on the three datasets, 3.7 to 5.1 percentage points higher than the baseline model. The system has strong generalization, high robustness, and reliable performance, offering a practical solution for multi - scenario text sentiment analysis. 

KEYWORDS

Sentiment Analysis, RoBERTa Model, Adversarial Training, Ablation Experiment, Pre-trained Language Model, Text Augmentation, Emotional Attention Mechanism

CITE THIS PAPER

Chuwei Wang, Zicheng Wang, Xihe Wang, Mingxing Wang. Design and Implementation of a Sentiment Analysis System Based on Deep Learning. Automation and Machine Learning (2026). Vol. 7, No. 1, 104-111. DOI: http://dx.doi.org/10.23977/autml.2026.070113.

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