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

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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
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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