Assessing Destination Brand Associations on Twitter: The case of Istanbul

Year 2023, Volume: 11 Issue: 4, 443 - 475, 11.12.2023

Cihangir Kasapoğlu , Ramazan Aksoy , Melih Başkol

https://doi.org/10.30519/ahtr.1116172

Abstract

The development of data mining has paved the way for studies that identify brand associations from user-generated content (UGC). However, the number of studies investigating destination associations with social media is limited. The aim of this study is to explore destination associations with UGC on Twitter and to show how data mining and sentiment analysis methods can be applied to destinations to elicit brand associations. In this study, 33,339 English-language tweets containing the word #Istanbul were collected over one year and analyzed using text mining (association rule analysis) and sentiment analysis. As a result of the study, a brand concept map (BCM) of what Twitter users associate with Istanbul was created and compared to other studies that measure associations using conventional methods. The main results show that users have positive associations with tourism in Istanbul. Unique and interesting associations (such as "cats") were observed compared to other previous studies that measured associations to destinations. Based on the study results, a method was proposed for measuring the image of a place brand by observing electronic word of mouth in social media.

Keywords

association rule, brand concept map, brand image, destination branding, sentiment analysis, text mining

References

• Aaker, D. (1991). Managing Brand Equity. New York.
• Aaker, D. A. (1992). The value of brand equity. Journal of Business Strategy, 13(4), 27-32. https://doi.org/10.1108/eb039503
• Agrawal, R, Mannila, H., Srikant, R., Toivonen, H., & Verkamo, I. (1996). Fast discovery of association rules. Advances in knowledge discovery and data mining, 12(1), 307-328.
• Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proc. of 20th International Conference on Very Large Data Bases, {VLDB’94} (Vol. 1215, pp. 487-499).
• Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
• Alarcón-Urbistondo, P., Rojas-de-Gracia, M. M., & Casado-Molina, A. (2021). Proposal for employing user-generated content as a data source for measuring tourism destination image. Journal of Hospitality & Tourism Research, 10963480211012756. https://doi.org/10.1177/10963480211012756
• Alexandris, K., Douka, S., Papadopoulos, P., & Kaltsatou, A. (2008). Testing the role of service quality on the development of brand associations and brand loyalty. Managing Service Quality, 18(3), 239–254. https://doi.org/10.1108/09604520810871865
• Alzate, M., Arce-Urriza, M., & Cebollada, J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning. Journal of Retailing and Consumer Services, 67, 102989. https://doi.org/10.1016/J.JRETCONSER.2022.102989
• Andéhn, M., Kazeminia, A., Lucarelli, A., & Sevin, E. (2014). User-generated place brand equity on Twitter: The dynamics of brand associations in social media. Place Branding and Public Diplomacy, 10(2), 132–144. https://doi.org/10.1057/pb.2014.8
• Anderson, J. R., & Bower, G. H. (1980). Human associative memory. Psychology Press. https://doi.org/10.2307/1421672
• Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897. https://doi.org/10.1016/S0160-7383(99)00030-4
• Başfırıncı, Ç. (2016). Analysing brand image through social network analysis: A research on Turkcell and Vodafone brand. İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi, 3(2), 25–50.
• Becken, S., Alaei, A. R., & Wang, Y. (2020). Benefits and pitfalls of using tweets to assess destination sentiment. Journal of Hospitality and Tourism Technology, 11(1), 19–34. https://doi.org/10.1108/JHTT-09-2017-0090
• Bell, R. C. (2005). The repertory grid technique. In F. Fransella (Ed.), International Handbook of Personal Construct Psychology (pp. 95–103). John Wiley & Sons Ltd. https://doi.org/10.1002/0470013370.ch9
• Bianchi, C., Pike, S., & Lings, I. (2014). Investigating attitudes towards three South American destinations in an emerging long haul market using a model of consumer-based brand equity (CBBE). Tourism Management, 42, 215–223. https://doi.org/10.1016/J.TOURMAN.2013.11.014
• Biel, A. (1992). How brand image drives brand equity. Journal of Advertising Research, 32(6), 6-12.
• Blanchard, S. J., Aloise, D., & Desarbo, W. S. (2017). Extracting summary piles from sorting task data. Journal of Marketing Research, 54(3), 398–414. https://doi.org/10.1509/jmr.15.0388
• Blasi, S., Brigato, L., & Sedita, S. R. (2020). Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining. Journal of Cleaner Production, 244, 118701. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.118701
• Böger, D., Kottemann, P., Meißner, M., & Decker, R. (2017). A mechanism for aggregating association network data: An application to brand concept maps. Journal of Business Research, 79, 90-106. https://doi.org/10.1016/j.jbusres.2017.05.025
• Brandt, C., de Mortanges, C. P., Bluemelhuber, C., & van Riel, A. C. R. (2011). Associative networks: A new approach to market segmentation. International Journal of Market Research, 53(2), 187-207. https://doi.org/10.2501/ijmr-53-2-187-208
• Brandt, C., & de Mortanges, C. P. (2011). City branding: A brand concept map analysis of a university town. Place Branding and Public Diplomacy, 7, 50-63. https://doi.org/10.1057/pb.2010.37
• Bui, V., Alaei, A. R., Vu, H. Q., Li, G., & Law, R. (2022). Revisiting tourism destination image: A holistic measurement framework using big data. Journal of Travel Research, 61(6), 1287–1307. https://doi.org/10.1177/00472875211024749
• Burkhalter, J. N., Wood, N. T., & Tryce, S. A. (2014). Clear, conspicuous, and concise: Disclosures and Twitter word-of-mouth. Business Horizons, 57(3), 319–328. https://doi.org/10.1016/J.BUSHOR.2014.02.001
• Cai, L. A. (2002). Cooperative branding for rural destinations. Annals of Tourism Research, 29(3), 720–742. https://doi.org/https://doi.org/10.1016/S0160-7383(01)00080-9
• Chen, H. (2012). Dark Web: Exploring and mining the dark side of the web. 2011 European Intelligence and Security Informatics Conference, Athens, Greece, (pp. 1-2). 10.1109/EISIC.2011.78.
• Chen, Y. C. (2017). The relationships between brand association, trust, commitment, and satisfaction of higher education institutions. International Journal of Educational Management, 31(7), 973–985. https://doi.org/10.1108/IJEM-10-2016-0212
• Cherfi, H., Napoli, A., & Toussaint, Y. (2003). Towards a text mining methodology using frequent itemsets and association rule extraction. In E. SanJuan (Ed.), Journées d’informatique Messine - JIM’03 (pp. 285--294). HAL-Inria. https://hal.inria.fr/inria-00107723
• Cho, E., Fiore, A. M., & Russell, D. W. (2015). Validation of a fashion brand image scale capturing cognitive, sensory, and affective associations: Testing its role in an extended brand equity model. Psychology & Marketing, 32(1), 28–48. https://doi.org/10.1002/MAR.20762
• Choi, S., Jeon, J., Subrata, B., & Kwon, O. (2015). An efficient estimation of place brand image power based on text mining technology. Journal of Intelligence and Information Systems, 21(2), 113–129. https://doi.org/https://doi.org/10.13088/jiis.2015.21.2.113
• Christodoulides, G., & de Chernatony, L. (2010). Consumer-based brand equity conceptualisation and measurement: A literature review. International Journal of Market Research, 52(1), 43-66. https://doi.org/10.2501/S1470785310201053
• Ci, C., & Choi, S. (2017). How to manage a city’s image using the brand concept map and network analysis: A case of Seoul. Quality and Quantity, 51(6), 2441–2456. https://doi.org/10.1007/s11135-016-0402-6
• Ciabuca, A. (2015). The development of a semantic differential scale for assessing the perceived image of citizens about Romanian Police Forces. Procedia - Social and Behavioral Sciences, 187, 28–33. https://doi.org/https://doi.org/10.1016/j.sbspro.2015.03.006
• Cian, L., & Cervai, S. (2011). The Multi‐Sensory Sort (MuSeS). Qualitative Market Research: An International Journal, 14(2), 138–159. https://doi.org/10.1108/13522751111120666
• Clarke, H., & Hassanien, A. (2020). An evaluation of Toronto’s destination image through tourist generated content on Twitter. International Journal of Customer Relationship Marketing and Management, 11(2), 1–16. https://doi.org/10.4018/IJCRMM.2020040101
• Claster, W., Pardo, P., Cooper, M., & Tajeddini, K. (2013). Tourism, travel and tweets: Algorithmic text analysis methodologies in tourism. Middle East J. of Management, 1(1), 81-99. https://doi.org/10.1504/mejm.2013.054071
• Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407
• Cornwell, T. B., Humphreys, M. S., & Kwon, Y. (2022). Shared brand equity. Journal of Advertising, 1-19. https://doi.org/10.1080/00913367.2022.2131656
• Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343-362. https://doi.org/10.1287/mksc.2015.0968
• Curlin, T., Jaković, B., & Miloloža, I. (2019). Twitter usage in tourism: Literature review. Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, 10(1), 102–119. https://doi.org/10.2478/bsrj-2019-0008
• di Pietro, L., di Virgilio, F., & Pantano, E. (2012). Social network for the choice of tourist destination: attitude and behavioural intention. Journal of Hospitality and Tourism Technology, 3(1), 60–76. https://doi.org/10.1108/17579881211206543
• Diaz-Garcia, J. A., Ruiz, M. D., & Martin-Bautista, M. J. (2022). A survey on the use of association rules mining techniques in textual social media. Artificial Intelligence Review, 56(2), 1175-1200. https://doi.org/10.1007/s10462-022-10196-3
• Dillon, W. R., Madden, T. J., Kirmani, A., & Mukherjee, S. (2001). Understanding what’s in a brand rating: A model for assessing brand and attribute effects and their relationship to brand equity. Journal of Marketing Research, 38(4), 415–429. https://doi.org/10.1509/jmkr.38.4.415.18910
• Dirsehan, T., & Kurtuluş, S. (2018). Measuring brand image using a cognitive approach: Representing brands as a network in the Turkish airline industry. Journal of Air Transport Management, 67, 85–93. https://doi.org/https://doi.org/10.1016/j.jairtraman.2017.11.010
• Divakaran, P. K. P., & Xiong, J. (2022). Eliciting brand association networks: A new method using online community data. Technological Forecasting and Social Change, 181, 121769. https://doi.org/https://doi.org/10.1016/j.techfore.2022.121769
• Dolnicar, S., Rossiter, J. R., & Grün, B. (2012). ‘Pick Any’ measures contaminate brand image studies. International Journal of Market Research, 54(6), 821–834. https://doi.org/10.2501/IJMR-54-6-821-834
• East, R., Hammond, K., & Lomax, W. (2008). Measuring the impact of positive and negative word of mouth on brand purchase probability. International Journal of Research in Marketing, 25(3), 215-224. https://doi.org/10.1016/j.ijresmar.2008.04.001
• Elsayed, M., Abdelwahab, A., & Ahdelkader, H. (2019). A proposed framework for improving analysis of big unstructured data in social media. 14th International Conference on Computer Engineering and Systems (ICCES), 61–65. https://doi.org/10.1109/ICCES48960.2019.9068154
• Feldman, R., & Sanger, J. (2006). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511546914
• Flores-Ruiz, D., Elizondo-Salto, A., & Barroso-González., M. D. L. O. (2021). Using social media in tourist sentiment analysis: A case study of Andalusia during the Covid-19 Pandemic. Sustainability, 13(7), 1–19.
• French, A., & Smith, G. (2013). Measuring brand association strength: A consumer based brand equity approach. European Journal of Marketing, 47(8), 1356–1367. https://doi.org/10.1108/03090561311324363
• Garay, L. (2019). #Visitspain. Breaking down affective and cognitive attributes in the social media construction of the tourist destination image. Tourism Management Perspectives, 32, 100560. https://doi.org/10.1016/J.TMP.2019.100560
• Gartner, W. C. (1994). Image Formation Process. Journal of Travel & Tourism Marketing, 2(2–3), 191–216. https://doi.org/10.1300/J073v02n02_12
• Gartner, W. C., & Ruzzier, M. K. (2010). Tourism destination brand equity dimensions: Renewal versus repeat market. Journal of Travel Research, 50(5), 471–481. https://doi.org/10.1177/0047287510379157
• Gensler, S., Völckner, F., Egger, M., Fischbach, K., & Schoder, D. (2015). Listen to your customers: Insights into brand image using online consumer-generated product reviews. International Journal of Electronic Commerce, 20(1), 112-141. https://doi.org/10.1080/10864415.2016.1061792
• Goffin, K., Lemke, F., & Koners, U. (2010). Repertory Grid Technique. In Identifying Hidden Needs: Creating Breakthrough Products (pp. 125–152). Palgrave Macmillan UK. https://doi.org/10.1057/9780230294486_6
• Gorin, A., Nedelko, A., Kosonogov, V., Vakhviyainen, M., Tugin, S., Moiseeva, V., Klucharev, V. & Shestakova, A. (2022). N400 correlate of brand associations. Journal of Economic Psychology, 90, 102506. https://doi.org/https://doi.org/10.1016/j.joep.2022.102506
• Han, H., Zhang, J., Yang, J., Shen, Y., & Zhang, Y. (2018). Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools and Applications, 77(16), 21265–21280. https://doi.org/10.1007/S11042-017-5529-5
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining Trends and Research Frontiers. Data Min, 585-631.
• Hand, D. J. (2007). Principles of data mining. Drug-Safety, 30(7), 621-622.
• Henderson, G. R., Iacobucci, D., & Calder, B. J. (1998). Brand diagnostics: Mapping branding effects using consumer associative networks. European Journal of Operational Research, 111(2), 306–327. https://doi.org/10.1016/S0377-2217(98)00151-9
• Hodeghatta, U. R., & Sahney, S. (2016). Understanding Twitter as an e-WOM. Journal of Systems and Information Technology, 18(1), 89–115. https://doi.org/10.1108/JSIT-12-2014-0074
• Hofstede, A., van Hoof, J., Walenberg, N., & de Jong, M. (2007). Projective techniques for brand image research. Qualitative Market Research: An International Journal, 10(3), 300–309. https://doi.org/10.1108/13522750710754326
• Howells, K., & Ertugan, A. (2017). Applying fuzzy logic for sentiment analysis of social media network data in marketing. Procedia Computer Science, 120, 664–670. https://doi.org/10.1016/j.procs.2017.11.293
• Hsieh, M. H. (2018). Measuring global brand equity using cross-national survey data. Journal of International Marketing, 12(2), 28–57. https://doi.org/10.1509/JIMK.12.2.28.32897
• Huete-Alcocer, N., & Hernandez-Rojas, R. D. (2022). Does local cuisine influence the image of a World Heritage destination and subsequent loyalty to that destination? International Journal of Gastronomy and Food Science, 27, 100470. https://doi.org/10.1016/j.ijgfs.2022.100470
• Hussein, N., Alashqur, A., & Sowan, B. (2015). Using the interestingness measure lift to generate association rules. Journal of Advanced Computer Science & Technology, 4(1), 156-162. https://doi.org/10.14419/jacst.v4i1.4398
• Ibrahim, M. M., & Elborsaly, A. A. (2022). Measuring tourism destination brand equity by using associative networks. European Journal of Business and Management Research, 7(3), 224–235. https://doi.org/10.24018/ejbmr.2022.7.3.1450
• Ivanov, S. H., Ilium, S. F., & Liang, Y. (2010). Application of destination brand molecule on destination image and brand perception: An exploratory study. Tourism, 58(4), 339–360. https://doi.org/10.2139/SSRN.1586263
• Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. https://doi.org/10.1002/asi.21149
• Jiang, Q., Chan, C.-S., Eichelberger, S., Ma, H., & Pikkemaat, B. (2021). Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists. Current Issues in Tourism, 24(17), 2501–2522. https://doi.org/10.1080/13683500.2021.1874312
• Jo, T. (2019). Text mining: Concepts, Implementation, and Big Data Challenge. Springer, Cham. https://doi.org/10.1007/978-3-319-91815-0
• John, D. R., Loken, B., Kim, K., & Monga, A. B. (2006). Brand concept maps: A methodology for identifying brand association networks. Journal of Marketing Research, 43(4), 549-563. https://doi.org/10.1509/jmkr.43.4.549
• Karayılmazlar, S., Bardak, T., Avcı, Ö., Kayahan, K., Karayılmazlar, A. S., Çabuk, Y., Kurt, R., & İmren, E. (2019). Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example. Turkish Journal of Forestry, 20(4), 447–457. https://doi.org/10.18182/tjf.609967
• Kavaratzis, M. (2004). From city marketing to city branding: Towards a theoretical framework for developing city brands. Place Branding, 1(1), 58–73. https://doi.org/10.1057/PALGRAVE.PB.5990005
• Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.2307/1252054
• Keller, K. L. (2013). Strategic Brand Management Building, Measuring, and Managing Brand Equity Global Edition (4th ed.). Pearson.
• Kim, D. D. E. (2017). A unified measure of media brand personality: Developing a media brand personality scale for multiple media. International Journal on Media Management, 19(3), 197–221. https://doi.org/10.1080/14241277.2017.1306531
• Kim, K., Park, O., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362–369. https://doi.org/10.1016/j.techfore.2017.01.001
• Kiran, P., & Vasantha, S. (2016). Analysing the role of user generated content on consumer purchase intention in the new era of social media and big data. Indian Journal of Science and Technology, 9(43), 1-7. https://doi.org/10.17485/ijst/2016/v9i43/104754
• Kladou, S., & Kehagias, J. (2014). Developing a structural brand equity model for cultural destinations. Journal of Place Management and Development, 7(2), 112–125. https://doi.org/10.1108/JPMD-03-2013-0007
• Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing, 35(4), 538-556. https://doi.org/10.1016/j.ijresmar.2018.08.002
• Kohn, E. (2020). “Kedi’ is the ‘Citizen Kane of Cat Documentaries – Review”. Indiewire. Retrieved 4 May, 2023, from https://www.indiewire.com/2017/02/kedi-review-cat-documentary-citizen-kane-1201781292/
• Koll, O., von Wallpach, S., & Uzelac, B. (2022). Stakeholder-oriented brand management: A Venn-diagram approach to monitor brand associations. European Management Journal. https://doi.org/10.1016/j.emj.2022.03.004
• Költringer, C., & Dickinger, A. (2015). Analyzing destination branding and image from online sources: A web content mining approach. Journal of Business Research, 68(9), 1836–1843. https://doi.org/10.1016/j.jbusres.2015.01.011
• Kumar, C. S. P., & Babu, L. D. D. (2021). Evolving dictionary based sentiment scoring framework for patient authored text. Evolutionary Intelligence, 14(2), 657–667. https://doi.org/10.1007/S12065-020-00366-Z
• Lai, K., & Cerpa, N. (2001). Support vs confidence in association rule algorithms. In Proceedings of the OPTIMA Conference, Curicó (pp. 1-14).
• Leelawat, N., Jariyapongpaiboon, S., Promjun, A., Boonyarak, S., Saengtabtim, K., Laosunthara, A., Yudha, A. K., & Tang, J. (2022). Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning. Heliyon, 8(10), e10894. https://doi.org/10.1016/J.HELIYON.2022.E10894
• Lim, K., & O’Cass, A. (2001). Consumer brand classifications: an assessment of culture‐of‐origin versus country‐of‐origin. Journal of Product & Brand Management, 10(2), 120–136. https://doi.org/10.1108/10610420110388672
• Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of Natural Language Processing (2. Ed.) (pp. 627-666).
• Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415-463). Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3223-4_13
• Liu, M. T., Liu, Y., Mo, Z., & Ng, K. L. (2021). Using text mining to track changes in travel destination image: the case of Macau. Asia Pacific Journal of Marketing and Logistics, 33(2), 371–393. https://doi.org/10.1108/APJML-08-2019-0477
• Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236–247. https://doi.org/10.1080/00913367.2017.1297273
• Lopes, A. A., Pinho, R., Paulovich, F., & Minghim, R. (2007). Visual text mining using association rules. Computers & Graphics, 31(3), 316–326. https://doi.org/10.1016/j.cag.2007.01.023
• Lopez, A., Guerra, E., Gonzalez, B., & Madero, S. (2020). Consumer sentiments toward brands: the interaction effect between brand personality and sentiments on electronic word of mouth. Journal of Marketing Analytics, 8(4), 203–223. https://doi.org/10.1057/S41270-020-00085-5
• Low, G. S., & Lamb, C. W. Jr. (2000). The measurement and dimensionality of brand associations. Journal of Product & Brand Management, 9(6), 350–370.
• Mak, A. H. N. (2017). Online destination image: Comparing national tourism organisation’s and tourists’ perspectives. Tourism Management, 60, 280–297. https://doi.org/10.1016/j.tourman.2016.12.012
• Marchand, A., Hennig-Thurau, T., & Wiertz, C. (2017). Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success. International Journal of Research in Marketing, 34(2), 336–354. https://doi.org/10.1016/J.IJRESMAR.2016.09.003
• Marine-Roig, E., & Anton Clavé, S. (2015). Tourism analytics with massive user-generated content: A case study of Barcelona. Journal of Destination Marketing & Management, 4(3), 162–172. https://doi.org/10.1016/J.JDMM.2015.06.004
• Matheson, J. L., & McCollum, E. E. (2008). Using metaphors to explore the experiences of powerlessness among women in 12-step recovery. Substance Use and Misuse, 43(8-9), 1027-1044. https://doi.org/10.1080/10826080801914287
• McNicholas, P. D., Murphy, T. B., & O’Regan, M. (2008). Standardising the lift of an association rule. Computational Statistics and Data Analysis, 52(10), 4712-4721. https://doi.org/10.1016/j.csda.2008.03.013
• 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
• Miner, G. D., Elder, J., & Nisbet, R. A. (2012). Practical text mining and statistical analysis for non-structured text data applications. In Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Elsevier Inc. https://doi.org/10.1016/C2010-0-66188-8
• Mishra, M. S., & Sharma, R. W. (2019). Brand crisis-sentiment analysis of user-generated comments about @Maggi on Facebook. Corporate Reputation Review, 22(2), 48–60. https://doi.org/10.1057/s41299-018-0057-4
• Mitra, S., & Jenamani, M. (2020). OBIM: A computational model to estimate brand image from online consumer review. Journal of Business Research, 114, 213–226. https://doi.org/10.1016/J.JBUSRES.2020.04.003
• Nadeau, J., Wardley, L. J., & Rajabi, E. (2021). Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter. Tourism and Hospitality Research, 22(1), 60–70. https://doi.org/10.1177/14673584211038317
• Nam, H., Joshi, Y., & Kannan, P. K. (2017). Harvesting brand information from social tags. Journal of Marketing, 81(4), 88–108. https://doi.org/10.1509/jm.16.0044
• Nautiyal, R., Albrecht, J. N., & Carr, A. (2022). Can destination image be ascertained from social media? An examination of Twitter hashtags. Tourism and Hospitality Research. https://doi.org/10.1177/14673584221119380
• Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543. https://doi.org/10.1287/mksc.1120.0713
• Nielsen, F. Å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. CEUR Workshop Proceedings.
• Oliverio, J. (2018). A survey of social media, big data, data mining, and analytics. Journal of Industrial Integration and Management, 3(03), 1850003. https://doi.org/10.1142/S2424862218500033
• Osgood, C. E. (1959). The representational model and relevant research methods. In I. Pool (Ed.), Trends in Content Analysis (pp. 33–38). Illinois Press.
• Özyirmidokuz, E. K. (2014). Mining unstructured Turkish economy news articles. Procedia Economics and Finance, 16, 320-328. https://doi.org/10.1016/s2212-5671(14)00809-0
• 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, 23(2), 241–264. https://doi.org/10.1007/s40558-021-00196-4
• Park, J., Yang, D., & Kim, H. Y. (2023). Text mining-based four-step framework for smart speaker product improvement and sales planning. Journal of Retailing and Consumer Services, 71, 103186. https://doi.org/10.1016/J.JRETCONSER.2022.103186
• Park, S. B., Kim, J., Lee, Y. K., & Ok, C. M. (2020). Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics. Tourism Management, 80, 104127. https://doi.org/10.1016/J.TOURMAN.2020.104127
• Perez Cabañero, C., Bigne, E., Ruiz Mafe, C., & Cuenca, A. C. (2020). Sentiment Analysis of Twitter in Tourism Destinations, 1–3. https://doi.org/10.4995/carma2020.2020.11621
• Phong, L. T., Nga, T. H., Hanh, N. T., & van Minh, N. (2020). Relationship between brand association and customer loyalty: The case of online retail industry. Management Science Letters, 10(7), 1543–1552. https://doi.org/10.5267/j.msl.2019.12.012
• Pich, C., Armannsdottir, G., & Dean, D. (2015). The elicitation capabilities of qualitative projective techniques in political brand image research. International Journal of Market Research, 57(3), 357–394. https://doi.org/10.2501/IJMR-2015-033
• Pike, S. (2009). Destination brand positions of a competitive set of near-home destinations. Tourism Management, 30(6), 857–866. https://doi.org/10.1016/J.TOURMAN.2008.12.007
• Plumeyer, A., Kottemann, P., Böger, D., & Decker, R. (2019). Measuring brand image: A systematic review, practical guidance, and future research directions. Review of Managerial Science, 13, 227-265. https://doi.org/10.1007/s11846-017-0251-2
• Qu, H., Kim, L. H., & Im, H. H. (2011). A model of destination branding: Integrating the concepts of the branding and destination image. Tourism Management, 32(3), 465–476. https://doi.org/10.1016/J.TOURMAN.2010.03.014
• Rahman, K., & Areni, C. S. (2016). The benefits of quantifying qualitative brand data: A mixed-method approach for converting free brand associations to a brand equity index. International Journal of Market Research, 58(3), 421–450. https://doi.org/10.2501/IJMR-2015-053
• Rajman, M., & Besançon, R. (1998). Text Mining: Natural Language techniques and Text Mining applications BT - Data Mining and Reverse Engineering: Searching for semantics. In S. Spaccapietra & F. Maryanski (Eds.), IFIP TC2 WG2.6 IFIP Seventh Conference on Database Semantics (DS-7) 7–10 October 1997, Leysin, Switzerland (pp. 50–64). Springer US. https://doi.org/10.1007/978-0-387-35300-5_3
• Ren, G., & Hong, T. (2017). Investigating online destination images using a topic-based sentiment analysis approach. Sustainability, 9(10), 1765. https://doi.org/10.3390/su9101765
• Romaniuk, J. (2014). Comparing methods of measuring brand personality traits. Journal of Marketing Theory and Practice, 16(2), 153–161. https://doi.org/10.2753/MTP1069-6679160205
• Sahin, S., & Baloglu, S. (2011). Brand personality and destination image of Istanbul. Anatolia, 22(1), 69–88. https://doi.org/10.1080/13032917.2011.556222
• Schnittka, O., Sattler, H., & Zenker, S. (2012). Advanced brand concept maps: A new approach for evaluating the favorability of brand association networks. International Journal of Research in Marketing, 29(3), 265–274. https://doi.org/10.1016/j.ijresmar.2012.04.002
• Shams, R., Alpert, F. & Brown, M. (2015). Consumer perceived brand innovativeness. European Journal of Marketing, 49(9/10), 1589–1615. https://doi.org/10.1108/EJM-05-2013-0240
• Shimada, K., Inoue, S., Maeda, H., & Endo, T. (2011). Analyzing tourism information on Twitter for a local city. 2011 First ACIS International Symposium on Software and Network Engineering, 61–66. https://doi.org/10.1109/SSNE.2011.27
• Spry, L., & Pich, C. (2021). Enhancing data collection methods with qualitative projective techniques in the exploration of a university’s brand identity and brand image. International Journal of Market Research, 63(2), 177–200. https://doi.org/10.1177/1470785320943045
• Statista. (2020). Largest cities in Europe in 2020. https://www.statista.com/statistics/1101883/largest-european-cities/.
• Stepchenkova, S., & Li, X. (Robert). (2014). Destination image: Do top-of-mind associations say it all? Annals of Tourism Research, 45, 46–62. https://doi.org/10.1016/j.annals.2013.12.004
• Surugiu, C., Tudorache, D. M., Aștefănoaiei, R. M., & Surugiu, M. R. (2021). Bucharest cultural heritage through the eyes of social media users. Anatolia, 33(4), 576-587. https://doi.org/10.1080/13032917.2021.1984960
• Tan, P. N., Steinbach, M., & Karpatne, K. (2018). Association analysis: Basic concepts and algorithms. In P.-N. Tan, M. Steinbach, A. Karpatne & V. Kumar (Eds.), Introduction to Data Mining (2nd ed., pp. 357–449). https://www-users.cs.umn.edu/~kumar001/dmbook/ch5_association_analysis.pdf
• Teichert, T. A., & Schöntag, K. (2010). Exploring consumer knowledge structures using associative network analysis. Psychology and Marketing, 27(4), 369-398. https://doi.org/10.1002/mar.20332
• Tseng, C., Wu, B., Morrison, A. M., Zhang, J., & Chen, Y. (2015). Travel blogs on China as a destination image formation agent: A qualitative analysis using Leximancer. Tourism Management, 46, 347–358. https://doi.org/10.1016/j.tourman.2014.07.012
• Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Discovering a tourism destination with social media data: BERT-based sentiment analysis. Journal of Hospitality and Tourism Technology, 13(5), 907–921. https://doi.org/10.1108/JHTT-09-2021-0259
• Vriens, M., Chen, S., & Schomaker, J. (2019). The evaluation of a brand association density metric. Journal of Product and Brand Management, 28(1), 104–116. https://doi.org/10.1108/JPBM-02-2018-1768
• Wong, C. U. I., & Qi, S. (2017). Tracking the evolution of a destination’s image by text-mining online reviews - the case of Macau. Tourism Management Perspectives, 23, 19–29. https://doi.org/10.1016/J.TMP.2017.03.009
• Wong, P. C., Whitney, P., & Thomas, J. (1999). Visualizing association rules for text mining. Proceedings of the IEEE Symposium on Information Visualization. https://doi.org/10.1109/infvis.1999.801866
• Yan, H., Ma, M., Wu, Y., Fan, H., & Dong, C. (2022). Overview and analysis of the text mining applications in the construction industry. Heliyon, 8(12), e12088. https://doi.org/10.1016/J.HELIYON.2022.E12088
• Yan, Y., Chen, J., & Wang, Z. (2020). Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations. Applied Geography, 123, 102306. https://doi.org/10.1016/J.APGEOG.2020.102306
• Yang, F. X., Li, X., & Choe, Y. (2022). What constitutes a favorable destination brand portfolio? Through the lens of coherence. Tourism Management, 90, 104480. https://doi.org/10.1016/j.tourman.2021.104480
• Zaltman, G. (1995). Metaphor elicitation method and apparatus (Patent No. 5,436,830).
• Zenker, S. (2014). Measuring place brand equity with the advanced Brand Concept Map (aBCM) method. Place Branding and Public Diplomacy, 10, 158-166. https://doi.org/10.1057/pb.2014.2
• Zenker, S., & Braun, E. (2015). Rethinking the measurement of place brands. In K. Mihalis, G. Warnaby & G. J. Ashworth (Eds.), Rethinking Place Branding Comprehensive: Brand Development for Cities and Regions (pp. 211–224). Springer, Cham. https://doi.org/10.1007/978-3-319-12424-7
• Zenker, S., & Braun, E. (2017). Questioning a “one size fits all” city brand: Developing a branded house strategy for place brand management. Journal of Place Management and Development, 10(3), 270–287. https://doi.org/10.1108/JPMD-04-2016-0018
• Zhang, Y., Chen, M., & Liu, L. (2015). A review on text mining. 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), 681–685. https://doi.org/10.1109/ICSESS.2015.7339149
• Zhang, Z., Luo, M., Luo, Z. & Niu, H. (2022). The international city image of Beijing: A quantitative analysis based on twitter texts from 2017–2021. Sustainability (Switzerland), 14(17), 10675. https://doi.org/10.3390/su141710675