Research on News Public Opinion Sentiment Analysis of Logistics Enterprises Based on TextCNN Model

This paper aims to build a logistics industry news public opinion sentiment analysis method that integrates the TextCNN model with attention mechanism, in order to improve the accuracy and efficiency of sentiment classification. The research content covers aspects such as model design, algorithm implementation, experimental verification, and result analysis. By combining TextCNN with attention mechanism, the potential advantages of this approach in improving sentiment analysis performance are explored. The experimental part demonstrates indicators such as accuracy, precision, recall, and F1 value of the model on the test set, and performs performance comparison analysis with other classic models. The research results show that the model exhibits good adaptability and stability in practical applications, and can effectively support logistics enterprises in public opinion monitoring, risk early warning, and brand management. The adaptability tests of the model in different scenarios and the assessment of its potential for promotion also show its economic and social value in practical commercial applications. Keywords Sentiment Analysis, TextCNN, Attention Mechanism, Logistics Industry, Public Opinion Monitoring. Zhai Jiahao "Research on News Public Opinion Sentiment Analysis of Logistics Enterprises Based on TextCNN Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-6 , December 2025, URL: https://www.ijtsrd.com/papers/ijtsrd98742.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/98742/research-on-news-public-opinion-sentiment-analysis-of-logistics-enterprises-based-on-textcnn-model/zhai-jiahao