IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v17y2017i4d10.1007_s40558-017-0095-2.html
   My bibliography  Save this article

Mining graphs from travel blogs: a review in the context of tour planning

Author

Listed:
  • Erum Haris

    (Universiti Sains Malaysia)

  • Keng Hoon Gan

    (Universiti Sains Malaysia)

Abstract

The tremendous expansion of user-generated content has made travel planning a tedious activity. The information overload disorients some travel knowledge seekers in choosing preferred places and visiting them in an appropriate order. Hence, a trip planning system mainly deals with collecting popular points of interests (POIs) in a region and proposing routes accordingly. In contrast to the various data sources of tour planning, travel blogs have been paid less attention. The massive text contained in these narratives manifests bloggers’ interactions with places that have been modeled with different graph representations. This review concentrates on the problem of extracting this graph-structured information from travel blogs with an explicit focus on POI graph, the fundamental trip planning structure that facilitates travel decision making. The study first discusses an analysis of travel blog mining and its dominant research themes, based on which three distinct categories of graphs are proposed. Each category of graphs is further explored using practical cases followed with a comparative analysis of existing works including their techniques, dataset and evaluation metrics. With the underlying idea of enhancing travel planning process with better POI graphs, the existing literature is critically assessed at the end along with a number of promising directions for future research.

Suggested Citation

  • Erum Haris & Keng Hoon Gan, 2017. "Mining graphs from travel blogs: a review in the context of tour planning," Information Technology & Tourism, Springer, vol. 17(4), pages 429-453, December.
    • Handle: RePEc:spr:infott:v:17:y:2017:i:4:d:10.1007_s40558-017-0095-2
    DOI: 10.1007/s40558-017-0095-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-017-0095-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-017-0095-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rosanna Leung & Huy Quan Vu & Jia Rong, 0. "Understanding tourists’ photo sharing and visit pattern at non-first tier attractions via geotagged photos," Information Technology & Tourism, Springer, vol. 0, pages 1-20.
    2. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    3. Rosanna Leung & Huy Quan Vu & Jia Rong, 2017. "Understanding tourists’ photo sharing and visit pattern at non-first tier attractions via geotagged photos," Information Technology & Tourism, Springer, vol. 17(1), pages 55-74, March.
    4. Aitor García-Pablos & Montse Cuadros & Maria Teresa Linaza, 2016. "Automatic analysis of textual hotel reviews," Information Technology & Tourism, Springer, vol. 16(1), pages 45-69, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ji Han & Jiabin Liu, 2018. "Urban Spatial Interaction Analysis Using Inter-City Transport Big Data: A Case Study of the Yangtze River Delta Urban Agglomeration of China," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
    2. Bilong Shen & Weimin Zheng & Kathleen M. Carley, 2018. "Urban Activity Mining Framework for Ride Sharing Systems Based on Vehicular Social Networks," Networks and Spatial Economics, Springer, vol. 18(3), pages 705-734, September.
    3. Liu, Zhaoyang & Hanley, Nick & Campbell, Danny, 2020. "Linking urban air pollution with residents’ willingness to pay for greenspace: A choice experiment study in Beijing," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    4. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    5. Chen, Yanguang, 2015. "The distance-decay function of geographical gravity model: Power law or exponential law?," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 174-189.
    6. Yeran Sun & Hongchao Fan & Ming Li & Alexander Zipf, 2016. "Identifying the city center using human travel flows generated from location-based social networking data," Environment and Planning B, , vol. 43(3), pages 480-498, May.
    7. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    8. Chun Li & Jianhua He & Xingwu Duan, 2020. "The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
    9. Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.
    10. Xinghua Liu & Qian Ye & Ye Li & Jing Fan & Yue Tao, 2021. "Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    11. Laiyun Wu & Samiul Hasan & Younshik Chung & Jee Eun Kang, 2021. "Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    12. Liu, Yilun & Zhu, A-Xing & Wang, Jingli & Li, Wenkai & Hu, Guohua & Hu, Yueming, 2019. "Land-use decision support in brownfield redevelopment for urban renewal based on crowdsourced data and a presence-and-background learning (PBL) method," Land Use Policy, Elsevier, vol. 88(C).
    13. Jiexiong Duan & Weixin Zhai & Chengqi Cheng, 2020. "Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede," IJERPH, MDPI, vol. 17(22), pages 1-14, November.
    14. Jing Yang & Disheng Yi & Jingjing Liu & Yusi Liu & Jing Zhang, 2019. "Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    15. Chi, Guanghua & Liu, Yu & Shi, Li & Gao, Yong, 2017. "Understanding the effects of administrative boundary in sampling spatially embedded networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 616-625.
    16. Jung, Paul H. & Thill, Jean-Claude, 2022. "Sea-land interdependence and delimitation of port hinterland-foreland structures in the international transportation system," Journal of Transport Geography, Elsevier, vol. 99(C).
    17. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    18. Wenjie Wu & Jianghao Wang, 2015. "Exploring city social interaction ties in the big data era: Evidence based on location-based social media data from China," ERSA conference papers ersa15p798, European Regional Science Association.
    19. Enrique Bigne & Carla Ruiz & Carmen Perez-Cabañero & Antonio Cuenca, 2023. "Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 281-314, March.
    20. Wang, Yufei & Hua, Mingzhuang & Chen, Xuewu & Chen, Wendong, 2023. "Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data," Journal of Transport Geography, Elsevier, vol. 110(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infott:v:17:y:2017:i:4:d:10.1007_s40558-017-0095-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.