Sentiment analysis of hotel online reviews using the BERT model and ERNIE model—Data from China

PLOS ONE RESEARCH ARTICLE Sentiment analysis of hotel online reviews using the BERT model and ERNIE model— Data from China Yu Wen1, Yezhang Liang ID2*, Xinhua Zhu3 1 Guilin Tourism University, Guilin, China, 2 Institute of Culture and Tourism, Guilin Tourism University, Guilin, China, 3 School of Computer Science, Guangxi Normal University, Guilin, China * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Wen Y, Liang Y, Zhu X (2023) Sentiment analysis of hotel online reviews using the BERT model and ERNIE model—Data from China. PLoS ONE 18(3): e0275382. https://doi.org/10.1371/ journal.pone.0275382 Editor: Sathishkumar V. E., Jeonbuk National University, REPUBLIC OF KOREA Abstract 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. Received: June 15, 2022 Accepted: September 15, 2022 1. Introduction Published: March 10, 2023 Copyright: © 2023 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was by the National Social Science Fund of China under Contract 17BTY063 awarded to YL and YW. Competing interests: The authors have declared that no competing interests exist. With the development of information technology and the increasing improvement of online booking and payment functions, online review data has become one of the important sources of competitive intelligence for enterprises [1]. In the hotel industry, more and more customers are booking hotel rooms through hotel websites or related online platforms, such as Hotels. com, Booking.com, and Ctrip [2]; more and more hotel customers are seeking advice from each other through online online reviews [3], and the effectiveness of traditional hotel advertising is declining, while the online The impact of hotel reviews is on the rise [4]. Online reviews are posted by customers who have experienced the hotel’s services, so for potential customers, online hotel reviews are more persuasive than the hotel’s own advertisements [5]. Online reviews will significantly influence customer attitudes, purchase decisions, and thus company performance [6–9]. A report on the Siteminder hotel services platform 2021 shows that more than two-thirds of of global travelers use travel review sites before booking, with 93% saying online reviews influence their booking decisions and 79% reading 6 to 12 reviews before making a purchase decision [10]. PLOS ONE | https://doi.org/10.1371/journal.pone.0275382 March 10, 2023 1 / 14 PLOS ONE Sentiment analysis of hotel online reviews Therefore, online reviews have important commercial value for improving hotel service quality and attracting consumer traffic, and it is important to improve the classifiability and accuracy of sentiment analysis of hotel online reviews. In practice, through the sentiment analysis of hotel’s online reviews, on the one hand, hotel managers can obtain customers’ experience evaluation and collect positive and negative comments to improve the personalized service and quality of the hotel [11]. In addition, by understanding customers’ emotional expressions and emotional analysis, they can capture the real inner emotions of users and understand the inner needs of customers, thus enabling hotels to better and more accurately capture their target groups, meet different needs and improve the core competitiveness of hotel brands. On the other hand, in the context of big data, sentiment analysis as an important tool can also help potential customers to make effective consumption decisions [12]. Emotion analysis based on hotel online reviews is a relatively new research topic that provides an intelligent application direction for personalizing customers’ accommodation needs. In addition, it creates space for developing high-quality tourism and improving tourist accommodation satisfaction [13]. However, there are many aspects of hotel online comment emotion [14], such as hotel price, star rating, location, room facilities, meals, staff and age group of consumers. For example, because language has multiple meanings, with positive ratings, negative ratings, and neutral ratings, it adds to the difficulty of collecting valid hotel online comment emotion [15]. However, it is impossible to accomplish this simply by manually organizing the huge amount of online reviews, and deep mining with the help of computers is needed to make more scientific decisions. To meet this challenge, some scholars have combined machine learning methods with sentiment analysis theories to classify the sentiment of online reviews in hotels by means of classification models, namely, plain Bayesian, K-nearest neighbor, support vector machine, logistic regression, and random forest [16], in order to achieve more accurate sentiment analysis. With the development and application of machine learning theory, the advantages of neural network technology in deep learning are becoming more and more obvious, and the fusion of multiple models has become a favorable guarantee for the accuracy of text sentiment classification, which can well solve the phenomenon of multiple meanings of the word and capture the depth characteristics of words in hotel web reviews [17]. This paper presents the results of crawling 16,000 customer reviews of Ctrip hotels in China using the BERT model to classify emotions in comment data. This approach can tap into the true feelings and internal needs of customers so that hotels can better and more accurately lock target groups, meet differentiated customer needs through market segmentation, and implement differentiated brand development strategies, thereby enhancing (...truncated)