Abstract
In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.
Highlights
Tourism research has entered the era of big data
We compared travel review text, this paper proposes a keyword semantic expansion method based on knowledge several keyword extraction methods and selected the TextRank method as having the best effect [50]
From the analysis presented in the previous section, we can see that the sentiment distribution online travel review texts is very uneven
Summary
Tourism research has entered the era of big data. Based on big data analysis, academia and industry are better positioned to understand and explore tourist behavior and the tourism market.Li et al [1] contend that big data analysis can provide sufficient data without introducing sampling bias, and can make up for the sample size limitations encountered by the survey data, therebyAppl. Tourism research has entered the era of big data. Based on big data analysis, academia and industry are better positioned to understand and explore tourist behavior and the tourism market. Li et al [1] contend that big data analysis can provide sufficient data without introducing sampling bias, and can make up for the sample size limitations encountered by the survey data, thereby. Sci. 2020, 10, 5275 enabling a better understanding of tourist behavior. Sivarajah 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
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