But Mason loved small cars, and he was convinced that one tailored to American tastes and driving conditions would not only be salable but could even turn a profit. The app also offers you the chance to follow travelers who have similar tastes to your own, read reviews from other travelers, and add your own insights to help fellow travelers in the future. This is not only a catharsis of dissatisfaction, but also content producers believe that it can help subsequent tourists avoid risks to a certain extent, and also reduce the consumption desire of future tourists. The main task of NSP is to capture the relationships between sentences in the future. The experiments prove that the BERT model is fully capable of the sentiment analysis task. And for the latter, the model is used to predict sentiment for unknown comments. For the former, the model is used to classify topics for unknown comments.
Experiment results show that the accuracy of the BERT model can reach 81.43% on the former and 87.29% on the latter. In the latter categories, the number of comments with the sentiment “Neutral” gradually dominates. Comments about parking were the least frequent of all categories, but more than 83.95% of comments about parking were positive. The relatively high-frequency categories (Food, Price, Crowd, Hygiene) in the dataset tend to be of negative polarity, the ratio of positive to negative values is generally small. The travel review dataset used in this paper has more mentions of food, price, crowdedness, and sanitary conditions of locations. We may not have the guaranteed lowest price, but are RV Roadside Assistance. Furthermore, experiments are executed on the sentiment analysis on the dataset without data augmentation and on the dataset with data augmentation. To illustrate the advantages of the data augmentation method used in this paper, the ablation experiments are conducts for the data augmentation method. Bidirectional Encoder Representation from Transformers, as a new language representation model, has been well-applied in this study, showing great advantages in sentiment classification and sentiment rating prediction of text. The quarry is a great innovation and it fits within the game.
The game was originally designed and programmed by Russian game designer Alexey Pajitnov while working at the Dorodnitsyn Computing Centre of the Academy of Science of the Soviet Union in Moscow. In the centre of Summerville, this 2-star venue is housed in a historic-style building. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. For example, the most frequent keyword in the data set is “food,” not that the word “food” appears too often in the data set, but this paper combines the words that describe food, such as it is not that the word “food” is too frequent in the dataset, but we will combine words that describe food, such as “delicious” and “unpalatable.” These words will be counted in the “Food” category, so our count is the sum of all words describing food in the dataset, with a total of 874 comments related to food. 10K-$50K Medical Coverage Medical coverage is what the company will pay out to you if you were injured or ill on your trip and needed medical care abroad. Most travel cots will be just fine for newborn infants, but you may prefer to get a smaller bassinet that’s specifically designed for young babies.
Given that travel agents and corporate users frequently access the portal, it must be optimized for repeated use, with tools designed to manage and track bookings efficiently. This paper finds four destination variables that users care about the most and are prone to negative emotions, which lead to negative WOM: food, prices, crowding, and sanitation. Tourists are more likely to evaluate destination satisfaction through the above four factors and are more likely to generate user-produced content with WOM effects in the online environment because of these key factors. Based on the above TF-IDF ten categories of comment data, this paper have experimented and tested the proposed dataset using the pre-trained BERT language model. The precision and recall of the model on Sentiment reached 87.29% and 88.64%, respectively. The precision and recall of the BERT model on Aspect reached 92.68% and 89.42%, respectively. This experiment evaluates two capabilities of the model, Aspect and Sentiment, respectively. Based on the original network, two linear layers are used in the last layer to output the category probabilities with cross entropy as the loss, and after training on the dataset we built, the sentiment classification of travel reviews can be finished.
