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

[1] Ahiler Development Agency Plans for Future. (2015). Tourism and Investment Opportunities in Cappadocia. https://www.ahika.gov.tr/ Ahiler Development Agency Plans for Future 2015 Tourism and Investment Opportunities in Cappadocia https://www.ahika.gov.tr/ Search in Google Scholar

[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 AlaeiA. R. BeckenS. StanticB. 2017 Sentiment analysis in tourism: Capitalizing on Big Data Journal of Travel Research 58 2 1 17 https://doi.org/10.1177/0047287517747753 Search in Google Scholar

[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 AlamoodiA. H. ZaidanB. B. ZaidanA. A. AlbahriO. S. MohammedK. I. MalikR. Q. AlmahdiE. M. ChyadM. A. TareqZ. AlbahriA. S. HameedH. AlaaM. 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 Search in Google Scholar

[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. AlaybaA. M. PaladeV. EnglandM. IqbalR. 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. Search in Google Scholar

[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 AlloghaniM. Al-JumeilyD. MustafinaJ. HussainA. AljaafA. J. 2019 A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science In BerryM. W. MohamedA. YapB. W. (Eds.), Supervised and Unsupervised Learning for Data Science: Unsupervised and Semi-Supervised Learning 3 22 Springer https://doi.org/10.1007/978-3-030-22475-2_1 Search in Google Scholar

[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 AppelG. GrewalL. HadiR. StephenA. 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 Search in Google Scholar

[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 AydınC. 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 Search in Google Scholar

[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 BalahadiaF. F. FernandoG. G. JuanatasI. 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 Search in Google Scholar

[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 BoiyE. MoensM.–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 Search in Google Scholar

[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 BuhalisD. LawR. 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 Search in Google Scholar

[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. BulutR. 2018 Tourism Sector Economic Contribution Ayrıntı: Göller Yöresi Ekonomi ve Kültür Dergisi (Detail: Lakes Region Economy and Culture Magazine) 5 62 54 57 Search in Google Scholar

[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 BüyükekeA. SökmenA. GencerC. 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 Search in Google Scholar

[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 ChengM. JinX. 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 Search in Google Scholar

[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 ChoiS. LeeJ. KangM.-G. MinH. ChangY.-S. YoonS. 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 Search in Google Scholar

[15] Condor Ferries. (2021). Online Travel Booking Statistics - Research & Planning. https://www.condorferries.co.uk/online-travel-booking-statistics Condor Ferries 2021 Online Travel Booking Statistics - Research & Planning https://www.condorferries.co.uk/online-travel-booking-statistics Search in Google Scholar

[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 DinaN. 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 Search in Google Scholar

[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 FangB. YeQ. KucukustaD. LawR. 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 Search in Google Scholar

[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 GeethaM. SinghaP. SinhaS. 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 Search in Google Scholar

[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 HastieT. TibshiraniR. FriedmanJ. 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer https://link.springer.com/book/10.1007/978-0-387-84858-7 Search in Google Scholar

[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 IslamM. J. WuQ. M. AhmadiM. Sid-AhmedM. 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 Search in Google Scholar

[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 JoachimsT. 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 Search in Google Scholar

[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 KietzmannJ. H. HermkensK. McCartyI. P. SilvestreB. 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 Search in Google Scholar

[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 KimD. SeoD. ChoS. KangP. 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 Search in Google Scholar

[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 KyawN. WaiT. 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 Search in Google Scholar

[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 LeungD. LawR. HoofH. BuhalisD. 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 Search in Google Scholar

[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 LuoT. ChenS. XuG. ZhouJ. 2013 Sentiment analysis In LuoT. ChenS. XuG. ZhouJ. (Eds.), Trust-based Collective View Prediction 53 68 Springer https://doi.org/10.1007/978-1-4614-7202-5_4 Search in Google Scholar

[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 MandaK. 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 Search in Google Scholar

[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 MathWorks 2016 Applying Supervised Learning https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/90221_80827v00_machine_learning_section4_ebook_v03.pdf Search in Google Scholar

[29] Maynard, D., & Funk, A. (2011, May 29 – June 2). Automatic detection of political opinions in Tweets [Conference Session]. 8^th Extended Semantic Web Conference, Heraklion, Greece. https://doi.org/10.1007/978-3-642-25953-1_8 MaynardD. FunkA. 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 Search in Google Scholar

[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 MedhatW. HassanA. KorashyH. 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 Search in Google Scholar

[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 NohhN. H. ZainuddinN. M. AnuarS. AzmiN. F. HassanW. A. 2019 Sentiment analysis towards hotel reviews Open International Journal of Informatics 7 Special Issue 2 1 19 https://oiji.utm.my/index.php/oiji/article/view/74 Search in Google Scholar

[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 ÖzenA. 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 Search in Google Scholar

[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 PaolantiM. ManciniA. FrontoniE. FelicettiA. MarinelliL. MarcheggianiE. PierdiccaR. 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 Search in Google Scholar

[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 PrabowoR. ThelwallM. 2009 Sentiment analysis: A combined approach Journal of Informetrics 3 2 143 157 https://doi.org/10.1016/j.joi.2009.01.003 Search in Google Scholar

[35] Republic of Turkey Ministry of Culture and Tourism. (2021). Kapadokya (Cappadocia). https://tanitma.ktb.gov.tr/TR-22783/kapadokya.html Republic of Turkey Ministry of Culture and Tourism 2021 Kapadokya (Cappadocia) https://tanitma.ktb.gov.tr/TR-22783/kapadokya.html Search in Google Scholar

[36] Republic of Turkey Ministry of Culture and Tourism. (2020). Turizm İstatistikleri (Tourism Statistics). https://yigm.ktb.gov.tr/ Republic of Turkey Ministry of Culture and Tourism 2020 Turizm İstatistikleri (Tourism Statistics) https://yigm.ktb.gov.tr/ Search in Google Scholar

[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 RibeiroF. N. AraújoM. GonçalvesP. GonçalvesM. A. BenevenutoF. 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 Search in Google Scholar

[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 SauraJ. R. Palos-SanchezP. GriloA. 2019 Detecting indicators for startup business success: Sentiment analysis using text data mining Sustainability 11 3 1 14 https://doi.org/10.3390/su11030917 Search in Google Scholar

[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 SchmunkS. HöpkenW. FuchsM. LexhagenM. 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 Search in Google Scholar

[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 SchuckertM. LiuX. LawR. 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 Search in Google Scholar

[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 SheldonP. J. 2006 Tourism information technology In DwyerL. ForsythP. (Eds.), International handbook on the economics of tourism 399 418 Edward Elgar Publishing https://www.elgaronline.com/display/9781843761044.00030.xml Search in Google Scholar

[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 SokolovaM. LapalmeG. 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 Search in Google Scholar

[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 SongQ. HuW. XieW. 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 Search in Google Scholar

[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/ 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/ Search in Google Scholar

[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/ 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/ Search in Google Scholar

[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 TingS. L. IpW. H. TsangA. 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 Search in Google Scholar

[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 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 Search in Google Scholar

[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 VermeulenI. E. SeegersD. 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 Search in Google Scholar

[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 VisaS. RamsayB. RalescuA. VanDerKnappE. 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 Search in Google Scholar

[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 WeiL. WeiB. WangB. 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 Search in Google Scholar

[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 XiangZ. GretzelU. 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 Search in Google Scholar

[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 XiangZ. DuQ. MaY. FanW. 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 Search in Google Scholar

[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 XiangZ. GretzelU. FesenmaierD. R. 2008 Semantic representation of tourism on the internet Journal of Travel Research 47 4 440 453 https://doi.org/10.1177/0047287508326650 Search in Google Scholar

[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 YeQ. ZhangZ. LawR. 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 Search in Google Scholar

[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 YiS. LiuX. 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 Search in Google Scholar

[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 YuL. PorwalA. HoldenE. –J. DentithM. 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 Search in Google Scholar