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.

The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rating, item average rating, user average rating, and MF, which improves the prediction accuracy. Then, based on the above algorithm, this article proposes a MF RS for preserving user privacy by using local differential privacy technology. In this algorithm, the rating data are normalized on the user side to reduce global sensitivity. Then, Laplace noise is added to sensitive data before it is sent to the aggregator. Finally, based on the disturbed data, rating prediction is realized by using the MF algorithm. The proposed method is compared with five well-known recommendation methods on four public datasets. The experimental results show that the proposed algorithm achieves better recommendation performance at the same privacy-preserving level.

Compared with traditional recommendation algorithms based on collaborative filtering and content, the sequential recommendation can better capture changes in user interests and recommend items that may be interacted with by the next time according to the user's historical interaction behaviors. Generally, there are several traditional methods for sequential recommendation: Markov Chain (MC) and Deep Neutral Network (DNN), both of which ignore the relationship between various behaviors and the dynamic changes of user interest in items over time. Furthermore, the early research methods usually deal with the user's historical interaction behavior in chronological order, which may cause the loss of partial preference information. According to the perspective that user preferences will change over time, this paper proposes a long and short-term sequential recommendation model with the enhanced self-attention network, RP-SANRec. The short-term intent module of RP-SANRec uses the Gated Recurrent Unit (GRU) to learn the comprehensive historical interaction sequence of the user to calculate the position weight information in the time order, which can be used to enhance the input of the self-attention mechanism. The long-term module captures the user's preferences through a bidirectional long and short-term memory network (Bi-LSTM). Finally, the user's dynamic interests and general preferences are fused, and the following recommendation result is predicted. This article applies the RP-SANRec model to three different public datasets under two evaluation indicators of HR@10 and NDCG@10. The extensive experiments proved that our proposed RP-SANRec model performs better than existing models.