Mining tourists’ destinations and preferences through LSTM-based text classification and spatial clustering using Flickr data

Lee, Hyejin; Kang, Youngok

doi:10.1007/s41324-021-00397-3

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Tang et al [ 58 ] found that LSTM can effectively capture the information of sentences. Lee et al [ 59 ] mine tourists' destinations and preferences through text classification and spatial clustering based on LSTM. The results show that this method has good results.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Text Classification Detector: A Bert-Based Mental Approach

Ding

Dou

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Text Classification Detector: A Bert-Based Mental Approach

Ding

Dou

et al. 2022

Computational Intelligence and Neuroscience

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“…Na primer kot indikator podobe (angl. image) oziroma prepoznavnosti destinacije (Donaire et al, 2014;Deng in Li, 2018;Deng et al, 2019;Taecharungroj in Mathayomchan, 2021), za analizo prostora interakcij med domačini in tujci (Kádár in Gede, 2013;Kádár, 2014;Paldino et al, 2015;Önder et al, 2016;Li et al, 2018), za prepoznavanje turističnih privlačnosti POI/AIO (Kisilevic et al, 2010;Hu et al, 2015;Peng in Huang, 2017;Giglio et al, 2019;Lee in Kang, 2021) ter za analizo mobilnosti obiskovalcev v prostoru (Jankowski et al, 2010;Vu et al, 2015;Mou et al, 2020;Park et al, 2020;Kádár in Gede, 2021;Han et al, 2021). Vzporedno z različnimi tipi raziskav se spreminja tudi metodologija.…”

Section: Pregled Literatureunclassified

Identifying visitor mobility patterns in Slovenia using Flickr data

Paliska¹,

Kerma²,

Drobne³

2022

geod. vest.

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Spatial analysis of the geographic positions of photos taken by tourists enables the monitoring, presentation, and planning of tourism activities in regard to location. Most photo-sharing websites offer the possibility of geo-tagging the photos to provide geographical information. In this paper, we use the geo-coordinates of photos from the Flickr platform to identify the locations of popular tourist attractions and destinations in Slovenia and analyse the mobility patterns of visitors between them. We analysed samples of 210,643 photos taken by 11,555 different Flickr users over a twelve- year period between 2007 and 2018. The results generally show that Slovenia is not an integrated and homogeneous tourism destination than the eastern part of Slovenia, and that there are individual subsystems of destinations with strong centers.

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“…The clustering method was used to generate tourist classifications. The researchers in [13] used spatial clustering methods to mine tourist destinations and preferences, in which the regions of tourist attractions for each tourism category were derived by the clustering algorithm. The researchers in [14] used a density-based spatial clustering algorithm to study tourist behavior, and by extracting the tourist behaviors, the tourism hot-spots were extracted as they related to tourist behavior.…”

Section: Introductionmentioning

confidence: 99%

“…As seen in [1][2][3][4][5][6][7], clustering algorithms have been used in tourism research for POI extraction, data mining, algorithm modeling, transportation behavior, etc. The other clustering methods in [8][9][10][11][12][13][14][15] indicated that spatial and attribute data of tourist attractions were the main targets that were used to generate proper tourism categories, extract tourist preferences, and recommend appropriate tourist destinations. The studies concerning tourist-attraction data extraction and tour-route algorithms that were used in [16][17][18][19][20][21][22][23][24][25][26][27][28][29] focused on three specific aspects.…”

Section: Introductionmentioning

confidence: 99%

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

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Mining tourists’ destinations and preferences through LSTM-based text classification and spatial clustering using Flickr data

Cited by 14 publications

References 30 publications

Short-Text Classification Detector: A Bert-Based Mental Approach

Short-Text Classification Detector: A Bert-Based Mental Approach

Identifying visitor mobility patterns in Slovenia using Flickr data

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

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