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A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

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  • Wafa Shafqat

    (Department of Computer Engineering, Jeju National University, Jeju 63243, Korea)

  • Yung-Cheol Byun

    (Department of Computer Engineering, Jeju National University, Jeju 63243, Korea)

Abstract

With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

Suggested Citation

  • Wafa Shafqat & Yung-Cheol Byun, 2019. "A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:320-:d:303653
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    References listed on IDEAS

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    2. Sehrish Malik & DoHyeun Kim, 2019. "Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data," Sustainability, MDPI, vol. 11(12), pages 1-26, June.
    3. 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.
    4. Lirong Chen & Wenli Li & Hao Chen & Shidao Geng, 2019. "Detection of Fake Reviews: Analysis of Sellers’ Manipulation Behavior," Sustainability, MDPI, vol. 11(17), pages 1-13, September.
    5. Hongyu Han & Yongshi Zhang & Jianpei Zhang & Jing Yang & Xiaomei Zou, 2018. "Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-11, August.
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