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Open AccessArticle

Research on Sentiment Classification of Online Travel Review Text

1
School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China
2
School of Mathematics and Computer Science, Tongling College, Tongling 244000, China
3
Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241003, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5275; https://doi.org/10.3390/app10155275
Received: 19 June 2020 / Revised: 22 July 2020 / Accepted: 27 July 2020 / Published: 30 July 2020
(This article belongs to the Special Issue Applied Machine Learning)
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. View Full-Text
Keywords: user generated content; sentiment analysis; classification; keyword extraction; text representation; sampling; machine learning; TripAdvisor user generated content; sentiment analysis; classification; keyword extraction; text representation; sampling; machine learning; TripAdvisor
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MDPI and ACS Style

Chen, W.; Xu, Z.; Zheng, X.; Yu, Q.; Luo, Y. Research on Sentiment Classification of Online Travel Review Text. Appl. Sci. 2020, 10, 5275. https://doi.org/10.3390/app10155275

AMA Style

Chen W, Xu Z, Zheng X, Yu Q, Luo Y. Research on Sentiment Classification of Online Travel Review Text. Applied Sciences. 2020; 10(15):5275. https://doi.org/10.3390/app10155275

Chicago/Turabian Style

Chen, Wen; Xu, Zhiyun; Zheng, Xiaoyao; Yu, Qingying; Luo, Yonglong. 2020. "Research on Sentiment Classification of Online Travel Review Text" Appl. Sci. 10, no. 15: 5275. https://doi.org/10.3390/app10155275

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