# Research on Sentiment Classification of Online Travel Review Text
## 1 Introduction  
Tourism research has entered the era of big data. Based on big data analysis, academia and industry are now better positioned to understand and explore tourist behavior and the tourism market. [PERSON] et al. [1] contend that big data analysis can provide sufficient data without introducing sampling bias, and can also make up for the sample size limitations encountered by the survey data, therebyenabling a better understanding of tourist behavior. [PERSON] et al. [2] argued that big data analysis can lead to new knowledge; subsequently, such analysis has become the mainstream method used to obtain useful information. From blogs and social media posts to online travel review sites, user-generated content (UGC) is one of the most important data sources for big data. UGC comprises insightful feedback that is spontaneously provided by users. This feedback information is widely available at little to no cost and can also be easily obtained [3]. Such feedback also has potential commercial value in fields such as targeted advertising, customer-company relationships, and brand communication [4; 5]. Online travel review sites, such as TripAdvisor, generate large amounts of text-based online travel review data, which constitute an important type of UGC [6]. Online review text data can help researchers and practitioners to correctly understand tourists' travel preferences and needs [7; 8]. The opinions expressed in user-generated comments also play an important role in influencing the choices of potential tourists [9; 10]. The characteristics of big data have complicated the process of knowledge extraction. The question of how to transform data into valuable knowledge has become crucial for big data applications [11; 12]. Previous research into online reviews has mainly focused on the quantitative ratings provided on the website, ignoring the text of online reviews [3]. Ratings cannot provide any information about the specific product characteristics that visitors like or dislike, and such information is typically included in the review text [5; 13]. In addition, many users are overwhelmed by the enormous amount of review information provided on travel online review sites. Researchers in other fields have also raised similar questions. [PERSON] et al. [14] noted that while urban traffic congestion is rapidly increasing, a city's rating score is insufficient to provide accurate information; however, comments or tweets may help travelers and traffic managers to understand all aspects of the city. As a result, it is necessary to establish an effective mechanism to help users identify the main content and emotions embedded in the review text [15]. Human emotions and emotional reasoning are understood to be important factors that influence consumer decision-making [16]. This makes sentiment analysis an effective method for mining the connotations of online travel review texts. Text sentiment analysis methods can be divided into dictionary-based methods [17], machine learning methods [13; 18], deep learning methods [19; 20], and hybrids of the above methods [21; 22]. [PERSON] et al. [8] contend that dictionary-based systems rely on the use of sentiment dictionaries and rule sets. Their article proposes that such methods are unable to adapt to the rapid increase in data volume in the era of big data, so it is necessary to develop more effective automated methods for sentiment analysis. Deep learning methods usually require a large amount of training data to fully realize their potential; this training data usually requires expensive class labeling [23]. Among machine learning methods, support vector machines (SVM) and naive Bayes are the most widely used in the tourism-related sentiment analysis context [13]. Compared with neural networks, SVM and naive Bayes require fewer class annotations to train the model [8]. Most studies on the subject have shown [18] that SVM-based sentiment analysis of text produces superior results relative to other machine learning methods. [PERSON] et al.