” This means that HTH Travel Insurance is a secondary provider and almost all of the plans and policies offered required you to have primary health insurance. In the statistical analysis step, this paper uses the Term Frequency-Inverse Document Frequency (TF-IDF) (Havrlant and Kreinovich, 2017) regression analysis to investigate the association between text data features and user travel plans. This combination of serial and parallel translation methods can generate new utterances while preserving the grammatical and structural features of the text. When the length of comments is too large, RNN will not be able to connect relevant information; LSTM cannot be used for parallel computing, which consumes a lot of time and space for experiments; Transformer lacks modeling of the time dimension, making the output of each position are very similar, which will eventually lead to poor performance in location classification. This seat offers some of the tallest harness slots of any convertible seat as well as a good fit for many newborns, making it is one of the only convertibles that will expire before kids outgrow it. There would be weekly offers of special trips and savings, with examples of luxury condos in Hawaii. It still offers incredible speed with Wi-Fi 6E, and its 5G mmWave technology will help you stay connected in even busy spaces where everyone else is trying to get online.

Porsche has also offered more attainable fun in the mid-engine Boxster and at the other end of the spectrum, supercar speed in the 10-cylinder Carrera GT. Lithium-Ion batteries are also becoming more common in solar applications. Exceptional No. Pet Policy for Hampton By Hilton Sheffield If you are thinking of bringing your pet (dog or cat) and want to know if pets are allowed at Hampton By Hilton Sheffield, please read the hotel pet policy. Backlight: Having an alarm with a backlight is convenient if you want to add a little more brightness to the room without having the turn on the light. If you’re like us at Hostelz, you’re probably a pretty healthy person who doesn’t want to blow your travel budget on insurance. What elements of travel motivate travelers to leave emotionally charged reviews on travel platforms? Online reviews as user-generated content (UGC) are a major part of eWOM (Ladhari and Michaud, 2015; Liu and Park, 2015; Banerjee and Chua, 2016; Anubha and Shome, 2020). Especially for intangible products and purchasing experiential goods (e.g., destinations, hotels, restaurants, and other tourism products), it is difficult to try out products before consumption. The eWOM communication of tourist attractions is extremely significant, not only because it determines the consumption behavior of potential tourists (Zhu et al., 2015; Mohammed Abubakar, 2016; Jalilvand and Heidari, 2017) but, more importantly, it is a more trustworthy information source, which has a more powerful communication effect than tourism enterprises’ propagation.

Word of mouth (WOM) plays a considerable role in influencing and forming consumers’ attitudes and behavioral intentions (Sen and Lerman, 2007; Reza Jalilvand and Samiei, 2012a,b; Reza Jalilvand et al., 2012). Since online communication and virtual interactions have become commonplace, the importance of electronic word of mouth (eWOM) or online WOM is increasing. The TF-IDF (Havrlant and Kreinovich, 2017) is used as a weighting criterion to measure the word vector represented by the document to calculate the importance of terms in the text. Aljedaani et al. (2022) conducted sentiment analysis on online reviews of six US airlines, mainly using four dictionary-based and deep learning models including CNN, LSTM, etc. In addition, sentiment analysis of travel reviews has some research basis, but the research methods basically stay in the traditional techniques based on word filtering, co-occurrence analysis and semantic clustering (Ainin et al., 2020; Jardim and Mora, 2022). However, as far as we know, there are few research results on sentiment analysis of tourism reviews using the BERT model. The current typical neural network learning methods are convolutional neural network (CNN) (Wang et al., 2022), recurrent neural network (RNN) (Al-Smadi et al., 2018), long-short-term memory network (LSTM) (Priyadarshini and Cotton, 2021), and Transformer (Naseem et al., 2020), etc. The Bidirectional Encoder Representation from Transformers (BERT) (Devlin et al., 2019) is different from previous models in that it is a deep, bi-directional, unsupervised language representation model that can be resumed on top of the latest pre-trained context-sensitive language representation work.

In this paper, BERT language model is used to perform category recognition sentence-level sentiment classification (Gao et al., 2019) and sentiment analysis (Vaswani et al., 2017; Devlin et al., 2019) on the travel comments. In present 10 comment categories about travel, the model performs sentiment analysis prediction for each comment and classifies them into Positive, Neutral, and Negative. Considering these advantages of BERT, this paper choose BERT as the sentiment analysis model for this paper. There have been some studies using various deep learning methods for sentiment analysis of reviews. Although there has been a long-term development in computer and other engineering disciplines to combine deep learning methods and textual information retrieval techniques for sentiment analysis (Zhao et al., 2017; Yiwen et al., 2022), its application in social sciences including tourism, linguistics, and communication is still scarce. Deep learning-based methods can effectively solve the problem of ignoring contextual semantics in traditional sentiment analysis methods. At present, sentiment analysis methods are mainly divided into two types: statistical-based and deep learning-based sentiment analysis methods. Martín et al. (2018) used hotel-related reviews to carry out comparative experiments using CNN and LSTM to conduct sentiment analysis texts.