Topic mining of tourist attractions based on a seasonal context aware LDA model

Abstract

With the rise of personalized travel recommendation in recent years, automatic analysis and summary of the tourist attraction is of great importance in decision making for both tourists and tour operators. To this end, many probabilistic topic models have been proposed for feature extraction of tourist attraction. However, existing state-of-the-art probabilistic topic models overlook the fact that tourist attractions tend to have distinct characteristics with respect to specific seasonal context. In this article, we contribute the innovative idea of using seasonal contextual information to refine the characteristics of tourist attractions. Along this line, we first propose STLDA, a season topic model based on latent Dirichlet allocation which can capture meaningful topics corresponding to various seasonal contexts for each attraction from a collection of attraction description documents. Then, an inference algorithm using Gibbs sampling is put forward to learn the posterior distributions and model parameters of our proposed model. In order to verify the effectiveness of STLDA model, we present a detailed experimental study using collected real-world textual data of tourist attractions. The experimental analysis results show that the superiority of STLDA over the basic LDA model in detecting the season-dependent topics and giving a representative and comprehensive summarization related to each tourist attraction. More importantly, it has great significance for improving the level of personalized attraction recommendation service.

A Review of Text Corpus-Based Tourism Big Data Mining

“…For example, the study [4] defined the global topic as "scenic spot" to filter noise semantics from tourism corpus, to explore the local topics of "scenic spot" by LDA, and then display the distribution of the attraction types or the local topics in the form of attraction maps. In this study [5], a season topic model based on LDA (STLDA) was proposed to explore the topic characteristics of attractions with seasonal features, which was of great significance for mining related topics with seasons and for personalized recommendations. In this study [57], a topic model was utilized to detect explicit interests by interactions between users for the following users' implicit interest profile building.…”

“…Benefits Methods [5] The topic features of attractions in the context of seasons are firstly explored, which are precisely at the fine-grained season levels.…”

“…These texts can give valuable insight for potential tourists, and assist them in optimizing destination choices and exploring travel routes, or for tourism practitioners to improve their services. Tourism platforms such as TripAdvisor and Ctrip now routinely provide an explosive amount of text data, which makes it possible to use deep learning [2], NLP [3], and other machine learning [4,5] and data mining techniques for tourism analysis. Studies [6,7] have shown that the competitiveness of the tourism industry dramatically relies on tourists' sentiment and opinions about the events occurring during…”

Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

“…The meaning of the expression between themes may be crossed. For the same topic, the higher the weight of the words it contains, the more relevant it is to the topic [29,30].…”

Sentiment-Aware Multi-modal Recommendation on Tourist Attractions

“…But they ignore the theme of attractions. Some other works focus on mining the themes of attractions to facilitate trip planning [4,9,15]. By considering the tourist topics and attraction themes, Leal et al [9] propose Parallel Topic Modelling to extract information and utilize semantic similarity to identify relevant recommendation.…”