Interpretable Sentiment Analysis of News Commentary Based on RoBERTa

在数字化信息高速传播的当下，实现对新闻评论精准且具备可解释性的情感分析，对舆情洞察与舆论引导极为关键。本研究运用RoBERTa模型，结合多尺度卷积神经网络(MSCNN)，深度剖析新闻评论中的情感倾向。同时，采用局部可解释的模型无关解释方法(LIME)，实现对模型预测结果的深度解释，可视化展示模型在处理新闻评论时对词汇和短语的关注重点，为模型决策提供清晰依据。实验结果表明，RoBERTa-MSCNN在新闻评论情感分析任务上取得了更优的性能，准确率达到83.34%，精确率为82.6%，召回率为84.67%，F1值提升至83.62%。同时，可解释性分析为用户理解模型输出提供了清晰的视角，有助于新闻媒体更有效地进行舆情监测与引导，为相关领域的研究与应用提供了有力支持。In the current era of high-speed digital information dissemination, accurate and interpretable emotional analysis of news comments is crucial for public opinion insight and guidance. In this study, RoBERTa model and multi-scale convolutional neural network (MSCNN) are used to analyze the emotional tendency of news commentary. At the same time, Local Interpretable Model-agnostic Explanations (LIME) is used to realize the in-depth interpretation of the model prediction results and visually display the model’s focus on words and phrases when processing news comments, providing a clear basis for the model’s decision-making. The experimental results show that RoBERTa-MSCNN has achieved superior performance in the task of sentiment analysis of news comments. Its accuracy rate reaches 83.34%, the precision rate is 82.6%, the recall rate is 84.67%, and the F1 score has been increased to 83.62%. At the same time, interpretability analysis provides a clear perspective for users to understand the model output, helps news media to monitor and guide public opinion more effectively, and provides strong support for research and application in related fields.