Guilin Tourism University, Guilin, China.
Institute of Culture and Tourism, Guilin Tourism University, Guilin, China.
PLoS One. 2023 Mar 10;18(3):e0275382. doi: 10.1371/journal.pone.0275382. eCollection 2023.
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.
通过使用神经网络模型 BERT 对酒店在线评论进行情感分析，证明该方法不仅可以帮助酒店网络平台充分了解客户需求，还可以帮助客户根据自己的需求和承受能力找到合适的酒店，帮助酒店推荐更加智能化。因此，通过预训练 BERT 模型，通过微调进行了多项情感分析实验，通过在实验过程中频繁调整参数，获得了具有较高分类准确性的模型。将 BERT 层作为词向量层，将输入的文本序列作为输入到 BERT 层进行向量转换。BERT 的输出向量通过相应的神经网络，然后通过 softmax 激活函数进行分类。ERNIE 是 BERT 层的增强。这两个模型都可以得到很好的分类结果，但后者的表现更好。ERNIE 比 BERT 具有更强的分类和稳定性，为旅游和酒店领域提供了一个有前途的研究方向。
