Word breakdown of hotel reviews.

The emotion analysis of hotel online reviews is discussed by using the neural network model BERT, which proves that this method can not only help hotel network platforms fully understand customer needs but also help customers find suitable hotels according to their needs and affordability and help hotel recommendations be more intelligent. Therefore, using the pretraining BERT model, a number of emotion analytical experiments were carried out through fine-tuning, and a model with high classification accuracy was obtained by frequently adjusting the parameters during the experiment. The BERT layer was taken as a word vector layer, and the input text sequence was used as the input to the BERT layer for vector transformation. The output vectors of BERT passed through the corresponding neural network and were then classified by the softmax activation function. ERNIE is an enhancement of the BERT layer. Both models can lead to good classification results, but the latter performs better. ERNIE exhibits stronger classification and stability than BERT, which provides a promising research direction for the field of tourism and hotels.本研究围绕酒店在线评论的情感分析任务展开探讨，采用神经网络模型BERT开展相关研究。实验结果证实，该方法不仅可助力酒店网络平台全面掌握客户需求，还能辅助消费者依据自身需求与经济承受能力挑选适配的酒店，推动酒店推荐服务更具智能化水平。基于此，本研究采用预训练BERT模型，通过微调操作开展多组情感分析实验，并在实验过程中反复调整模型参数，最终得到具备高分类精度的情感分析模型。该模型以BERT层作为词向量层，将输入文本序列作为BERT层的输入以完成向量变换；BERT的输出向量经对应神经网络处理后，通过softmax激活函数完成分类任务。ERNIE是针对BERT层的增强版本，两类模型均可取得优异的分类效果，但ERNIE的表现更为出色。相较于BERT，ERNIE拥有更强的分类能力与稳定性，可为旅游与酒店领域提供极具前景的研究方向。