# Research on Sentiment Classification of Online Travel Review Text
## 4 Discussion  
Sentiment analysis is a mainstream technology that employs social media analysis strategies to analyze customer feedback and comments. Conducting sentiment analysis based on websites such as TripAdvisor is desirable because a large number of free datasets can be obtained from such websites for large-scale research, while such large-scale data cannot easily be obtained via traditional research methods. Big data provides a new type of data for use in tourism research, and also puts forward higher requirements for data processing. Currently, few studies have been conducted on the applicability and accuracy of sentiment analysis methods in the tourism research literature. In addition,  
Figure 14: Sydney_Opera_House receiver operating characteristic curve. Figure 13: Tower_of_London receiver operating characteristic curve. contemporary research ignores the possibility of integrating human knowledge, such as knowledge graphs, into existing methods in order to improve the text sentiment analysis performance. Big data is characterized by a huge data volume, and the speed and accuracy requirements for sentiment analysis are becoming steadily higher [8]. Therefore, the prospect of developing suitable and efficient sentiment analysis methods for specific types of big data in the tourism context is a highly valuable proposition. The obtained sentiment analysis results based on TripAdvisor review text can be applied to multiple fields. For example, they can help sightseeing spots, restaurants, or hotels to explain comments and adopt corresponding countermeasures, which can in turn provide decision makers and customers with better decision-making information. Similarly, this approach can also be used to study theoretical issues related to customer satisfaction (for example, whether a tour guide service would improve the tourist experience). However, existing studies [43; 44] have found that the key features of the review text differ substantially depending on which websites they are drawn from, and that it is therefore necessary to conduct sentiment analysis research on one specific website at a time. Therefore, research into machine learning sentiment analysis methods for TripAdvisor review texts will aid in the development of tourism research utilizing these texts. Compared with vocabulary sentiment analysis, one of the advantages of machine learning sentiment analysis is that it does not require humans to create a dictionary; this is beneficial because the production of such a dictionary is a time-consuming and laborious process. In addition, machine learning methods achieve more accurate performance on larger amounts of training data than can be obtained using vocabulary sentiment analysis [8]. Feature extraction is a key issue in the application of machine learning to the field of sentiment analysis [24]. Accordingly, this study designed and implemented a sentiment classification method based on the semantic expansion of text keywords that both increases the classification features and improves the accuracy of sentiment analysis, thereby providing a novel solution for machine learning sentiment analysis. In terms of the specific details of the work of this article, in order to improve the accuracy of sentiment analysis conducted on online travel review texts, this study conducted extensive research work on the classification problems caused by the data features of online review texts. First, most online review texts are short texts, which makes it difficult to obtain more accurate sentiment classification results. To solve this problem, we designed a text keyword semantic expansion method based on a knowledge graph. In this part of the research, the present study compared three typical text keyword extraction methods and provided keyword extraction methods that are suitable for online travel review texts. In addition, based on Microsoft Knowledge Graph, the semantics of text keywords were expanded, and richer and more valuable sentiment classification features were constructed.