# Research on Sentiment Classification of Online Travel Review Text
## 1 Introduction
Several existing works [8; 13] have already summarized the sentiment analysis methods adopted by the academic community in the tourism context prior to 2016; therefore, this article only summarizes the relevant literature published after 2017 in Table 1. Among these works, [PERSON] et al. [36] extracted customer feedback from two online platforms and carried out sentiment analysis and opinion mining, verifying that SVM is best able to solve the problem of sentiment analysis compared with other related methods. [PERSON] et al. [37] used topic modeling technology to identify hidden information and other aspects, then performed sentiment analysis on classified hotel review text sentences. [PERSON] et al. [38] performed sentiment analysis on TripAdvisor's review data using Leximancer. [PERSON] et al. [39] applied statistical analysis methods to a large number of consumer review texts obtained from Expedia, enabling these authors to understand the experiences of hotel guests and analyze their association with satisfaction. [PERSON] et al. [40] selected and compared three of the best-performing sentiment analysis methods to quantify respondents' views on travel in China. [PERSON] et al. [41] further proposed a sentiment classification method based on mixed attributes. By capturing implicit word relationships and combining domain-specific knowledge, these authors were able to obtain a fine-grained emotional orientation of online consumer reviews. Finally, [PERSON] et al. [42] used the AFINN dictionary (a lexicon based on unigrams) to extract the sentiments from comments left by Airbnb guests and derive a quality score from those comments. An analysis of the above literature reveals that the academic community has carried out fruitful work in the field of sentiment analysis, particularly as regards the feature selection of SVM. Although these related topics have been extensively researched, certain specific types of content,such as online travel review texts for TripAdvisor, still present some challenges when using sentiment analysis [43]. This is because the key features of reviews vary significantly from site to site, meaning that it cannot be assumed that the sentiment analysis method and findings of a certain site will be applicable to all other review sites [44]. On the subject of the sentiment analysis of online travel review texts, most existing sentiment analysis models fail to comprehensively and effectively consider the data characteristics of travel review texts during the modeling process. Online travel review texts have their own inherent characteristics. Most review texts are short, which makes it difficult to extract keywords; in addition, the sentiment distribution of short texts is uneven [45] (for example, the texts with the highest and lowest scores are comparatively few). These characteristics make it difficult for accurate sentiment analysis results to be obtained for online travel review texts [46]. In addition, the accuracy of existing automated sentiment analysis methods is also low [13]. In order to deal with the sentiment analysis-related challenges brought about by the data features of online travel review texts, this study converted the sentiment analysis of online travel review texts into a multi-classification process based on machine learning methods, and further conducted research on sentiment classification methods for such texts. In order to improve the classification accuracy of online travel review texts, the current research mainly addresses the following problems related to previous research. The main contributions of the paper include:  
* Based on the word similarity calculation results, the present study compares three keyword extraction methods and provides the most suitable keyword extraction method for online travel review text. * After considering the sparse features of online travel review texts, this paper expands the semantics of text keywords based on Microsoft Knowledge Graph in order to build richer and more valuable classification features.