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.
By judging whether the start-point and end-point of a trajectory conform to the user’s behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user’s habits. Second, k−1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k-anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.