基于CNN-BiLSTM融合多头自注意力机制的电商评价情感分析探究

针对传统单一的基于机器学习的情感分析方法在特征提取以及语义理解方面效果不尽如人意的问题。构建一种基于CNN-BiLSTM融合多头自注意力机制的电商评价情感分析模型，能够更好地处理文本中的长距离依赖关系和捕捉情感信息的语义关系，从而提高模型的鲁棒性和泛化能力，进而提高商家对消费者评论的情感理解和评价准确性。基于一个中文电商公开数据集对模型进行了实验，并将其与其他模型进行了比较。实验结果表明，该模型的精确度、准确度、召回率和F1值等指标均优于其他模型。

Research on Emotional Analysis of E-commerce Evaluation Based on CNN-BiLSTM Fusion Multi-head-Self-Attention Mechanism

In response to the shortcomings of traditional single-machine learning-based sentiment analysis methods in feature extraction and semantic understanding, a novel e-commerce sentiment analysis model was developed. This model integrated a combination of CNN-BiLSTM and multi-head self-attention mechanisms, aiming to better address long-distance dependencies in the text and captured the semantic relationships of emotional information. This enhanced the model''s robustness and generalization capabilities, consequently improving merchants'' understanding of consumer sentiments and the accuracy of evaluations.Experiments were conducted on a publicly available Chinese e-commerce dataset, and the model was compared with other existing models. The experimental results indicate that this model outperforms others in terms of precision, accuracy, recall, and F1 score, among other metrics.