A tourism destination recommender system using users’ sentiment and temporal dynamics

Zheng, Xiaoyao; Luo, Yonglong; Sun, Liping; Ji, Zhang; Chen, Fulong

doi:10.1007/s10844-018-0496-5

Cited by 67 publications

(40 citation statements)

References 32 publications

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“…2 A tourism destination recommender system using users' sentiment and temporal dynamics. [5] SVD++, HTF, SVD, TopicMD…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis for Product Recommendation Using Random Forest

Khanvilkar¹,

Vora²

2018

IJET

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Analysis of sentiments is to analyze the natural language and to find the emotions, express by the human beings. The idea behind sentiment analysis is to determine polarity of textual opinion given by person. Sentiment Analysis is useful in product recommendations. Based on the reviews given by the user; the products can be recommended to another user. Major product websites are using sentiment analysis to understand the popularity and problems with the product. Sentiment analysis mainly formulated as two class classification problem, positive and negative. Sentiment analysis using ordinal classification gives more clear idea about sentiments. The proposed system determines polarity of reviews given by users, using ordinal classification. The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.

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“…2 A tourism destination recommender system using users' sentiment and temporal dynamics. [5] SVD++, HTF, SVD, TopicMD…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis for Product Recommendation Using Random Forest

Khanvilkar¹,

Vora²

2018

IJET

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“…In tourism, the application of sentiment classification techniques can help manage obtain tourist sentiment tendency and opinions in real time, thus making appropriate measures. For example, the study [95] proposed a tourist destination recommendation system by analyzing and evaluating the user's sentiment tendency; the study [96] explored the sustainable tourism development path through the sentiment analysis of the user reviews of the shared bicycle system in Spain; and in this study [97], a visual analysis system was designed to analyze regional trends and sentiment changes in visitors.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…In addition, some scholars have also considered the context of travel in the recommendation process, such as seasons, holidays, etc. In the study [95], a text mining technique was used to calculate the user's sentiment tendency toward the destination, and the influence of time elements such as seasons and holidays on the tourists' sentiments were considered comprehensively to promote the tourism recommendation system greatly. Based on the literature research of tourism recommendation system [151,152], we summarize the general framework of the tourism recommendation system based on text mining (shown in Figure 2).…”

Section: Tourist Profilementioning

confidence: 99%

“…It enhances our understanding of the aspects of dissatisfaction in text reviews. STM [95] It employs text mining techniques to access sentiment tendency which is incorporated into an enhanced Singular Value Decomposition (SVD++) model for model amendment also with the temporal influence, such as seasons and holidays on the tourists' sentiments.…”

Section: Probability Generative Model;mentioning

confidence: 99%

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A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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“…Currently, a significant amount of research has been carried out on tourism analysis and applications based on sentiment analysis. Zheng [1] proposed a tourism destination recommender system by analyzing and quantifying users' sentiment tendency. Ren [2] proposed a topic-based sentiment analysis approach to measure online destination image.…”

Section: Introductionmentioning

confidence: 99%

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

et al. 2018

Sustainability

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Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.

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A tourism destination recommender system using users’ sentiment and temporal dynamics

Cited by 67 publications

References 32 publications

Sentiment Analysis for Product Recommendation Using Random Forest

Sentiment Analysis for Product Recommendation Using Random Forest

A Review of Text Corpus-Based Tourism Big Data Mining

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

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