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Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists Cover

Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists

Open Access
|Jan 2024

References

  1. Ahiler Development Agency Plans for Future. (2015). Tourism and Investment Opportunities in Cappadocia. https://www.ahika.gov.tr/
  2. Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment analysis in tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 1–17. https://doi.org/10.1177/0047287517747753
  3. Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Mohammed, K. I., Malik, R. Q., Almahdi, E. M., Chyad, M. A., Tareq, Z., Albahri, A. S., Hameed, H., & Alaa, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Systems with Applications, 167(2021), 1s13. https://doi.org/10.1016/j.eswa.2020.114155
  4. Alayba, A. M., Palade, V., England, M., & Iqbal, R. (2017, April 3–5). Arabic Language Sentiment Analysis on Health Services [Conference Session]. International Workshop on Arabic Script Analysis and Recognition (ASAR), Nancy, France. https://ieeexplore.ieee.org/document/8067771#full-text-section.
  5. Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2019). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In M. W. Berry, A. Mohamed & B. W. Yap (Eds.), Supervised and Unsupervised Learning for Data Science: Unsupervised and Semi-Supervised Learning (pp.3–22). Springer. https://doi.org/10.1007/978-3-030-22475-2_1
  6. Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(2020), 79–95. https://doi.org/10.1007/s11747-019-00695-1
  7. Aydın, C. (2019). Classification of the fire station requirement with using machine learning algorithms. International Journal of Information Technology and Computer Science, 11(1), 24–30. https://www.mecs-press.org/ijitcs/ijitcs-v11-n1/IJITCS-V11-N1-3.pdf
  8. Balahadia, F. F., Fernando, G. G., & Juanatas, I. C. (2016, May 9–11). Teacher’s performance evaluation tool using opinion mining with sentiment analysis [Conference Session]. IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia. https://ieeexplore.ieee.org/document/7519384
  9. Boiy, E., & Moens, M.–F. (2008). A machine learning approach to sentiment analysis in multilingual Web texts. Information Retrieval, 12(2008), 526–558. https://doi.org/10.1007/s10791-008-9070-z
  10. Buhalis, D., & Law, R. (2008). Progress in Information Technology and Tourism Management: 20 Years on and 10 Years After the Internet – The State of eTourism Research. Tourism Management, 29(4), 609–23. https://doi.org/10.1016/j.tourman.2008.01.005
  11. Bulut, R. (2018). Tourism Sector Economic Contribution. Ayrıntı: Göller Yöresi Ekonomi ve Kültür Dergisi (Detail: Lakes Region Economy and Culture Magazine), 5(62), 54–57.
  12. Büyükeke, A., Sökmen, A., & Gencer, C. (2020). Metin madenciliği ve duygu analizi yöntemleri ile sosyal medya verilerinden rekabetçi avantaj elde etme: Turizm sektöründe bir araştırma (Gaining competitive advantage from social media data with text mining and sentiment analysis methods: A research in the tourism sector). Journal of Tourism and Gastronomy Studies, 8(1), 322–335. https://www.researchgate.net/publication/340324271_Metin_Madenciligi_ve_Duygu_Analizi_Yontemleri_ile_Sosyal_Medya_Verilerinden_Rekabetci_Avantaj_Elde_Etme_Turizm_Sektorunde_Bir_Arastirma_Gaining_Competitive_Advantage_from_Social_Media_Data_with_Text_M
  13. Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76(Part A), 58–70. https://doi.org/10.1016/j.ijhm.2018.04.004
  14. Choi, S., Lee, J., Kang, M.-G., Min, H., Chang, Y.-S., & Yoon, S. (2017). Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks. Methods, 129(2017), 50–59. https://doi.org/10.1016/j.ymeth.2017.07.027
  15. Condor Ferries. (2021). Online Travel Booking Statistics - Research & Planning. https://www.condorferries.co.uk/online-travel-booking-statistics
  16. Dina, N. Z. (2020). Tourist sentiment analysis on TripAdvisor using text mining: A case study using hotels in Ubud, Bali. African Journal of Hospitality, Tourism and Leisure, 9(2), 1–10. https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_12_vol_9_2__2020_indonesia.pdf
  17. Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52(2026), 498–506. https://doi.org/10.1016/j.tourman.2015.07.018
  18. Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels – An empirical analysis. Tourism Management, 61(2017), 43–54. https://doi.org/10.1016/j.tourman.2016.12.022
  19. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. https://link.springer.com/book/10.1007/978-0-387-84858-7
  20. Islam, M. J., Wu, Q. M., Ahmadi, M., & Sid-Ahmed, M. A. (2007, November 21–23). Investigating the performance of Naïve-Bayes classifiers and K-Nearest Neighbor classifiers [Conference Session]. International Conference on Convergence Information Technology. https://ieeexplore.ieee.org/xpl/conhome/4420216/proceeding
  21. Joachims, T. (1998, April 21–23). Text categorization with support vector machines: learning with many relevant features [Conference Session]. European Conference on Machine Learning, Chemnitz, Germany. https://link.springer.com/chapter/10.1007/BFb0026683
  22. Kietzmann, J. H., Hermkens, K., McCarty, I. P., & Silvestre, B. S. (2011). Social Media? Get Serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–51. https://doi.org/10.1016/j.bushor.2011.01.005
  23. Kim, D., Seo, D., Cho, S., & Kang, P. (2019). Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Information Sciences, 477(2019), 15–29. https://doi.org/10.1016/j.ins.2018.10.006
  24. Kyaw, N., & Wai, T. (2019, November 6–7). inferring user preferences using reviews for rating prediction [Conference Session]. International Conference on Advanced Information Technologies (ICAIT), Yangon, Myanmar. https://ieeexplore.ieee.org/document/8921179
  25. Leung, D., Law, R., Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: a literature review. Journal of Travel & Tourism Marketing, 30(1–2), 3–22. https://doi.org/10.1080/10548408.2013.750919
  26. Luo, T., Chen, S., Xu, G., & Zhou, J. (2013). Sentiment analysis. In T. Luo, S. Chen, G. Xu & J. Zhou (Eds.), Trust-based Collective View Prediction (pp. 53–68). Springer. https://doi.org/10.1007/978-1-4614-7202-5_4
  27. Manda, K. R. (2019). Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods. [Master’s thesis, Blekinge Institute of Technology.] DiVA portal. https://www.diva-portal.org/smash/get/diva2:1335995/FULLTEXT02
  28. MathWorks. (2016). Applying Supervised Learning. https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/90221_80827v00_machine_learning_section4_ebook_v03.pdf
  29. Maynard, D., & Funk, A. (2011, May 29 – June 2). Automatic detection of political opinions in Tweets [Conference Session]. 8th Extended Semantic Web Conference, Heraklion, Greece. https://doi.org/10.1007/978-3-642-25953-1_8
  30. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
  31. Nohh, N. H., Zainuddin, N. M., Anuar, S., Azmi, N. F., & Hassan, W. A. (2019). Sentiment analysis towards hotel reviews.sOpen International Journal of Informatics, 7(Special Issue 2), 1–19. https://oiji.utm.my/index.php/oiji/article/view/74
  32. Özen, A. (2021). Evaluation of tourist reviews on TripAdvisor for the protection of the world heritage sites: Text mining approach. Journal of Multidisciplinary Academic Tourism, 6(1), 37–46. https://doi.org/10.31822/jomat.876175
  33. Paolanti, M., Mancini, A., Frontoni, E., Felicetti, A., Marinelli, L., Marcheggiani, E., & Pierdicca, R. (2021). Tourism destination management using sentiment analysis and geo-location information: A deep learning approach. Information Technology & Tourism, 24(2021), 241–264. https://doi.org/10.1007/s40558-021-00196-4
  34. Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143–157. https://doi.org/10.1016/j.joi.2009.01.003
  35. Republic of Turkey Ministry of Culture and Tourism. (2021). Kapadokya (Cappadocia). https://tanitma.ktb.gov.tr/TR-22783/kapadokya.html
  36. Republic of Turkey Ministry of Culture and Tourism. (2020). Turizm İstatistikleri (Tourism Statistics). https://yigm.ktb.gov.tr/
  37. Ribeiro, F. N., Araújo, M., Gonçalves, P., Gonçalves, M. A., & Benevenuto, F. (2016). SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(23), 1–29. https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0085-1
  38. Saura, J. R., Palos-Sanchez, P., & Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability, 11(3), 1–14. https://doi.org/10.3390/su11030917
  39. Schmunk, S., Höpken, W., Fuchs, M., & Lexhagen, M. (2014, January 21–24). Sentiment analysis; extracting decision-relevant knowledge from UGC [Conference Session]. Information and Communication Technologies in Tourism, Dublin, Ireland. https://www.researchgate.net/publication/260601169_Sentiment_Analysis_Extracting_Decision-Relevant_Knowledge_from_UGC
  40. Schuckert, M., Liu, X., & Law, R. (2015). A segmentation of online reviews by language groups: How English and non-English speakers rate hotels differently. International Journal of Hospitality Management, 48(2015), 143–149. https://doi.org/10.1016/j.ijhm.2014.12.007
  41. Sheldon, P. J. (2006). Tourism information technology. In L. Dwyer & P. Forsyth (Eds.), International handbook on the economics of tourism (pp.399–418). Edward Elgar Publishing. https://www.elgaronline.com/display/9781843761044.00030.xml
  42. Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
  43. Song, Q., Hu, W., & Xie, W. (2002). Robust support vector machine with bullet hole image classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 32(4), 440–448. https://ieeexplore.ieee.org/document/1176893
  44. Statista. (2021a). Number of Monthly Active Twitter Users Worldwide from 1st Quarter 2010 to 1st Quarter 2019. https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/
  45. Statista. (2021b). Number of User Reviews and Opinions on Tripadvisor Worldwide from 2014 to 2019. https://www.statista.com/statistics/684862/Tripadvisor-number-of-reviews/
  46. Ting, S. L., Ip, W. H., & Tsang, A. H. C. (2011). Is Naïve Bayes a good classifier for document classification? International Journal of Software Engineering and Its Applications, 5(3), 37–46. https://www.researchgate.net/publication/266463703_Is_Naive_Bayes_a_Good_Classifier_for_Document_Classification
  47. UNWTO. (2001). eBusiness for tourism: Practical guidelines for destinations and businesses. World Tourism Organization. https://www.e-unwto.org/doi/epdf/10.18111/9789284404599?role=tab
  48. Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123–127. https://doi.org/10.1016/j.tourman.2008.04.008
  49. Visa, S., Ramsay, B., Ralescu, A., & VanDerKnapp, E. (2011, April 16–17). Confusion Matrix-based Feature Selection [Conference Session]. The 22nd Midwest Artificial Intelligence and Cognitive Science Conference, Ohio, United States of America. https://openworks.wooster.edu/facpub/88
  50. Wei, L., Wei, B., & Wang, B. (2012). Text classification using support vector machine with mixture of kernel. Journal of Software Engineering and Applications, 5(12B), 55–58. https://www.scirp.org/journal/paperinformation.aspx?paperid=26881
  51. Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. https://doi.org/10.1016/j.tourman.2009.02.016
  52. Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58(2017), 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
  53. Xiang, Z., Gretzel, U., & Fesenmaier, D. R. (2008). Semantic representation of tourism on the internet. Journal of Travel Research, 47(4), 440–453. https://doi.org/10.1177/0047287508326650
  54. Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert System with Applications, 36(3), 6527–6535. https://doi.org/10.1016/j.eswa.2008.07.035
  55. Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(2020), 621–634. https://doi.org/10.1007/s40747-020-00155-2
  56. Yu, L., Porwal, A., Holden, E. –J., & Dentith, M. C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Computer & Geosciences, 45(2012), 229–239. https://doi.org/10.1016/j.cageo.2011.11.019
DOI: https://doi.org/10.2478/ejthr-2023-0015 | Journal eISSN: 2182-4924 | Journal ISSN: 2182-4916
Language: English
Page range: 188 - 197
Submitted on: Sep 6, 2023
Accepted on: Aug 28, 2023
Published on: Jan 31, 2024
Published by: Polytechnic Institute of Leiria
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2024 Özge Barış-Tüzemen, Samet Tüzemen, Ali Kemal Çelik, published by Polytechnic Institute of Leiria
This work is licensed under the Creative Commons Attribution 4.0 License.