IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i10p1765-d113711.html
   My bibliography  Save this article

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Author

Listed:
  • Gang Ren

    (College of Business Administration, Pusan National University, Busan 46241, Korea)

  • Taeho Hong

    (College of Business Administration, Pusan National University, Busan 46241, Korea)

Abstract

With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com . By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

Suggested Citation

  • Gang Ren & Taeho Hong, 2017. "Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1765-:d:113711
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/10/1765/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/10/1765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    3. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    6. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    7. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    8. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    9. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    10. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    11. Xiang, Zheng & Gretzel, Ulrike, 2010. "Role of social media in online travel information search," Tourism Management, Elsevier, vol. 31(2), pages 179-188.
    12. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ziye Shang & Jian Ming Luo, 2022. "Topic Modeling for Hiking Trail Online Reviews: Analysis of the Mutianyu Great Wall," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    2. Qin Li & Shaobo Li & Jie Hu & Sen Zhang & Jianjun Hu, 2018. "Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    3. Berny Carrera & Jae-Yoon Jung, 2018. "SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    4. Chen Liu & Li Tang & Wei Shan, 2018. "An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews," Sustainability, MDPI, vol. 10(5), pages 1-15, May.

    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. Jun Shao & Xuesong Chang & Alastair M. Morrison, 2017. "How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    2. Joan-Francesc Fondevila-Gascón & Gaspar Berbel & Mònica Muñoz-González, 2019. "Experimental Study on the Utility and Future of Collaborative Consumption Platforms Offering Tourism Related Services," Future Internet, MDPI, vol. 11(3), pages 1-12, March.
    3. Kritana Prueksakorn & Cheng-Xu Piao & Hyunchul Ha & Taehyeung Kim, 2015. "Computational and Experimental Investigation for an Optimal Design of Industrial Windows to Allow Natural Ventilation during Wind-Driven Rain," Sustainability, MDPI, vol. 7(8), pages 1-22, August.
    4. Hualin Xie & Jinlang Zou & Hailing Jiang & Ning Zhang & Yongrok Choi, 2014. "Spatiotemporal Pattern and Driving Forces of Arable Land-Use Intensity in China: Toward Sustainable Land Management Using Emergy Analysis," Sustainability, MDPI, vol. 6(6), pages 1-17, May.
    5. Stephan E. Maurer & Andrei V. Potlogea, 2021. "Male‐biased Demand Shocks and Women's Labour Force Participation: Evidence from Large Oil Field Discoveries," Economica, London School of Economics and Political Science, vol. 88(349), pages 167-188, January.
    6. Tie Hua Zhou & Ling Wang & Keun Ho Ryu, 2015. "Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation," Sustainability, MDPI, vol. 7(5), pages 1-18, May.
    7. T. Karski, 2019. "Opinions and Controversies in Problem of The So-Called Idiopathic Scoliosis. Information About Etiology, New Classification and New Therapy," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 12(5), pages 9612-9616, January.
    8. Sung-Won Park & Sung-Yong Son, 2017. "Cost Analysis for a Hybrid Advanced Metering Infrastructure in Korea," Energies, MDPI, vol. 10(9), pages 1-18, September.
    9. Wesley Mendes-da-Silva, 2020. "What Makes an Article be More Cited?," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 24(6), pages 507-513.
    10. Martin Valtierra-Rodriguez & Juan Pablo Amezquita-Sanchez & Arturo Garcia-Perez & David Camarena-Martinez, 2019. "Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors," Mathematics, MDPI, vol. 7(9), pages 1-19, August.
    11. Akca Yasar & Gokhan Ozer, 2016. "Determination the Factors that Affect the Use of Enterprise Resource Planning Information System through Technology Acceptance Model," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(10), pages 1-91, September.
    12. Julián Miranda & Angélica Flórez & Gustavo Ospina & Ciro Gamboa & Carlos Flórez & Miguel Altuve, 2020. "Proposal for a System Model for Offline Seismic Event Detection in Colombia," Future Internet, MDPI, vol. 12(12), pages 1-17, December.
    13. Wisdom Akpalu & Mintewab Bezabih, 2015. "Tenure Insecurity, Climate Variability and Renting out Decisions among Female Small-Holder Farmers in Ethiopia," Sustainability, MDPI, vol. 7(6), pages 1-16, June.
    14. Wei Chen & Shu-Yu Liu & Chih-Han Chen & Yi-Shan Lee, 2011. "Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games," Games, MDPI, vol. 2(1), pages 1-13, March.
    15. David Harborth & Sebastian Pape, 2020. "Empirically Investigating Extraneous Influences on the “APCO” Model—Childhood Brand Nostalgia and the Positivity Bias," Future Internet, MDPI, vol. 12(12), pages 1-16, December.
    16. Ping Wang & Jie Wang & Guiwu Wei & Cun Wei, 2019. "Similarity Measures of q-Rung Orthopair Fuzzy Sets Based on Cosine Function and Their Applications," Mathematics, MDPI, vol. 7(4), pages 1-23, April.
    17. Peterson, Willis L., 1973. "Publication Productivities Of U.S. Economics Department Graduates," Staff Papers 14105, University of Minnesota, Department of Applied Economics.
    18. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    19. He-Yau Kang & Amy H. I. Lee & Tzu-Ting Huang, 2016. "Project Management for a Wind Turbine Construction by Applying Fuzzy Multiple Objective Linear Programming Models," Energies, MDPI, vol. 9(12), pages 1-15, December.
    20. Vasilyeva, Olga, 2021. "Agro-food clusters in the Republic of Kazakhstan: assessment and prospects of development," Economic Consultant, Roman I. Ostapenko, vol. 34(2), pages 13-20.

    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:gam:jsusta:v:9:y:2017:i:10:p:1765-:d:113711. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.