Araştırma Makalesi
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Liman Marka Değeri Ölçümü: Liman Sosyal Medya Mesajlari Üzerine Bir Sentiment Analizi

Yıl 2020, Sayı: 65, 85 - 106, 24.07.2020

Öz

Son yıllarda limanlar, sosyal medya mesajlarını liman kullanıcılarına yaymaya yönelik markalaşma faaliyetleri üzerinde odaklanmaktadırlar. Limanlar için sosyal medyanın marka oluşturmak maksadıyla güçlü bir pazarlama aracı olarak kullanıldığı markalaşma stratejilerinde marka değerini inşa etmek önemli bir hedeftir. Ancak, araştırmacılar marka değeri üzerinde başarılı bir sosyal medya analizi uygulamakta zorlanmaktadırlar. Bu sebeple, çalışma sentiment analizi ile liman marka değerinin araştırmacılar tarafından nasıl ölçebileceğini araştırmaktadır. Bir diğer amaç ise, sosyal medya istatistiklerinin liman marka değeri başarısını ölçebilecek kadar iyi olup olmadığını ortaya çıkarmaktır. Avrupa, Orta Doğu, Uzak Doğu Asya ve Amerika'da yerleşik 47 limandan toplanan 63.699 tweet’ten oluşan örneklem sentiment analizinde kullanılmak üzere değerlendirilmiştir. En önemli bulgular şunlardır: (a) Sosyal medya analizi, B2B pazarlamasında son yıllarda tercih edilen araştırma tekniği olmasına rağmen, sentiment analizi gibi daha kompleks araştırma yöntemleriyle desteklenmelidir. (b) Bazı operasyonel dezavantajlar yüzünden müşteriler gözünde konvansiyonel limanların marka değeri yeşil limanlara oranla daha olumludur. (c) Sosyal medya analizi hangi liman hizmetinin marka değerinin maksimize ettiğinin görülmesi açısından da yardımcı olabilmektedir.

Kaynakça

  • Aaker, D.A. (1992). The value of brand equity. Journal of Business Strategy, 4(13), 27-32.
  • Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS), 26(3), 1-34.
  • Alessia, D., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3).
  • Agerri, R., & García-Serrano, A. (2010). Q-WordNet: Extracting Polarity from WordNet Senses. Proceedings of the LREC, 2300-2305.
  • Baccianella, S., Esuli, A., & Sebastiani, F. (2010). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the LREC, 10, 2200-2204.
  • Barnes, N. G., Leschault, A. M., & Andonian, J. (2012). Social media surge by the 2012 Fortune 500: Increase use of blogs, Facebook, Twitter and more. Retrieved from http://www.umassd.edu/cmr/socialmedia/2012fortune500/
  • Benamara, F., Cesarano, C., Picariello, A., Recupero, D. R., & Subrahmanian, V. S. (2007). Sentiment analysis: Adjectives and adverbs are better than adjectives alone. Proceedings of the ICWSM, 3, 1-7.
  • Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Journal of Marketing Research, 49(2), 192-205.
  • Bollegala, D., Weir, D., & Carroll, J. (2012). Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1719-1731.
  • Cawsey, T., & Rowley, J. (2016). Social media brand building strategies in B2B companies. Marketing Intelligence & Planning, 34(6), 754-776.
  • Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media: How online consumer reviews evolve. Journal of interactive marketing, 25(2), 85-94.
  • Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575–600.
  • De Vries, L., Gensler, S., & Leeflang, P. S. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83-91.
  • Duncan, T., & Moriarty, S. (1998). A communication based marketing model for managing relationships. Journal of Marketing, 62(2), 1–13.
  • Emarketer. (2010). Complimentary eMarketer Report: Seven guidelines for achieving ROI from social media. Retrieved from http://www.emarketer.com/blog/index.php/complimentary-emarketer-report-guidelines-achieving-roi-social-media/#qLcSBtK2HzEdrxHA.99
  • Erşahin, B., Aktaş, Ö., Kilinc, D., & Erşahin, M. (2019). A hybrid sentiment analysis method for Turkish. Turkish Journal of Electrical Engineering & Computer Sciences, 27(3), 1780-1793.
  • Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of the LREC, 6, 417-422.
  • 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.
  • Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833-5841.
  • Hatzivassiloglou, V., & Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. Proceedings of the 18th Conference on Computational linguistics, 1, 299-305.
  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168-177.
  • Farquhar, P. H. (1989). Managing brand equity. Marketing Research, 1, 24-33.
  • Kalampokis, E., Karamanou, A., Tambouris, E., & Tarabanis, K. A. (2016). Applying brand equity theory to understand consumer opinion in social media. J. UCS, 22(5), 709-734.
  • Kamps, J., Marx, M., Mokken, R. J., & De Rijke, M. (2004). Using WordNet to measure semantic orientations of adjectives. Proceedings of the LREC, 4, 1115-1118.
  • Karjaluoto, H., Ulkuniemi, P., Huotari, L., Saraniemi, S., & Mäläskä, M. (2015). Analysis of content creation in social media by B2B companies. Journal of Business & Industrial Marketing, 6(30), 761–770
  • Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22.
  • Keller, K. L. (2009). Building strong brands in a modern marketing communications environment. Journal of Marketing Communications, 15(2–3), 139–155.
  • Kim, S. M., & Hovy, E. (2004). Determining the sentiment of opinions. Proceedings of the 20th international conference on Computational Linguistics, 1 – 8.
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.
  • Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.) Thousand Oaks, CA: Sage Publications.
  • Kumar, V., & Mirchandani, R. (2012). Winning with data: Increasing the ROI of social media marketing. MIT Sloan Management Review, 54(1), 55–61.
  • Lai, C. S., Chiu, C. J., Yang, C. F., & Pai, D. C. (2010). The effects of corporate social responsibility on brand performance: The mediating effect of industrial brand equity and corporate reputation. Journal of Business Ethics, 95(3), 457-469.
  • Labrecque, L. I., Zanjani, S. H., & Milne, G. R. (2012). Authenticity in online Communications. In Online consumer behavior: Theory and research in social media, advertising, and e-tail (pp. 133-155). Cham: Springer.
  • Leone, R. P., Rao, V. R., Keller, K. L., Luo, A. M., McAlister, L., & Srivastava, R. (2006). Linking brand equity to customer equity. Journal of Service Research, 9(2), 125-138.
  • Li, S., Xue, Y., Wang, Z., & Zhou, G. (2013). Active learning for cross-domain sentiment classification. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Beijing, 2127-2133
  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
  • Liu, S., Li, F., Li, F., Cheng, X., & Shen, H. (2013). Adaptive co-training SVM for sentiment classification on tweets. Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 10, 2079-2088.
  • Mahajan, V., Rao, V. R., & Srivastava, R. K. (1994). An approach to assess the importance of brand equity in acquisition decisions. Journal of Product Innovation Management, 11(3), 221-235.
  • Meng, X., Wei, F., Liu, X., Zhou, M., Xu, G., & Wang, H. (2012). Cross-lingual mixture model for sentiment classification. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 7, 572-581.
  • Michaelidou, N., Siamagka, N. T., & Christodoulides, G. (2011). Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial Marketing Management, 40, 1153–1159.
  • Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444-456.
  • Moilanen, K., Pulman, S., & Zhang, Y. (2010). Packed feelings and ordered sentiments: Sentiment parsing with quasi-compositional polarity sequencing and compression. Proceedings of the WASSA Workshop at ECAI, 8, 36-43.
  • Mohammad, S., Dunne, C., & Dorr, B. (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2, 599-608.
  • Mueller, K., Garg, S., Nam, J. E., Berg, T., & McDonnell, K. T. (2011). Can computers master the art of communication?: A focus on visual analytics. IEEE Computer Graphics and Applications, 31(3), 14-21.
  • Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016). SemEval-2016 task 4: Sentiment analysis in Twitter. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 5, 1-18.
  • Netzer, Y., Tenenboim-Weinblatt, K., & Shifman, L. (2014). The construction of participation in news websites: A five-dimensional model. Journalism Studies, 15(5), 619-631.
  • Pan, S. J., Ni, X., Sun, J. T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. Proceedings of the 19th international conference on World Wide Web, 4, 751-760.
  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, 7, 271.
  • Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9-28). London: Springer.
  • Rapp, A., Beitelspacher, L. S., Grewal, D., & Hughes, D. E. (2013). Understanding social media effects across seller, retailer, and consumer interactions. Journal of the Academy of Marketing Science, 41(5), 547–566.
  • Spire Inc. (2018). Spire Reports 2018. Prnewswire web sitesinden erişildi: https://www.prnewswire.com/news-releases/spire-reports-2018-results-300750871.html
  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1631-1642.
  • Strapparava, C., & Valitutti, A. (2004). Wordnet affect: an affective extension of wordnet. Proceedings of the LREC, 4, 1083-1086.
  • Strapparava, C., Valitutti, A., & Stock, O. (2006). The affective weight of lexicon. Proceedings of the LREC, 5, 423-426.
  • Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies' Twitter communications. Industrial Marketing Management, 43(5), 873-881.
  • Taylor, D. G., Lewin, J. E., & Strutton, D. (2011). Friends, fans, and followers: Do ads work on social networks? How gender and age shape receptivity. Journal of Advertising Research, 51(1), 258–275.
  • Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198-215.
  • Wan, X. (2012). A comparative study of cross-lingual sentiment classification. Proceedings of the 2012 International Conferences on Web Intelligence and Intelligent Agent Technology, 1, 24-31.
  • Wan, X. (2009). Co-training for cross-lingual sentiment classification. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 1, 235-243.
  • Wiebe, J., Wilson, T., Bruce, R., Bell, M., & Martin, M. (2004). Learning subjective language. Computational Linguistics, 30(3), 277-308.
  • Wiersema, F. (2013). The B2B agenda: The current state of B2B marketing and a look ahead. Industrial Marketing Management, 42(4), 470–488.

Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages

Yıl 2020, Sayı: 65, 85 - 106, 24.07.2020

Öz

In the last decades, ports are focusing their branding activities on disseminating the social media messages to the port users. The building of brand equity for seaports is an essential goal in various branding strategies, within which social media has become the marketing tool to create a strong brand. However, researchers struggle to implement successful social media analytics on brand equity. For this reason, this paper investigates how practitioners may measure brand equity of their seaports by performing social media sentiment analysis. Another aim of this study is to find whether the social media statistics are good enough to measure the success of the port brand equity. Sentiment analysis is employed to assess a sample of 63.699 tweets by 45 seaports, selected from Europe, Middle East, Far East Asia, and America. The most important findings are as follows: (a) Although social media analytics is the preferred research technique for marketing in the last decades, it should be supported by more complex research methods such as sentiment analysis. (b) Comparing the green ports, conventional ports have more positive effects on customers in terms of operational activities (c) The social media analytics may help to understand which port services maximize the branding equity.

Kaynakça

  • Aaker, D.A. (1992). The value of brand equity. Journal of Business Strategy, 4(13), 27-32.
  • Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS), 26(3), 1-34.
  • Alessia, D., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3).
  • Agerri, R., & García-Serrano, A. (2010). Q-WordNet: Extracting Polarity from WordNet Senses. Proceedings of the LREC, 2300-2305.
  • Baccianella, S., Esuli, A., & Sebastiani, F. (2010). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the LREC, 10, 2200-2204.
  • Barnes, N. G., Leschault, A. M., & Andonian, J. (2012). Social media surge by the 2012 Fortune 500: Increase use of blogs, Facebook, Twitter and more. Retrieved from http://www.umassd.edu/cmr/socialmedia/2012fortune500/
  • Benamara, F., Cesarano, C., Picariello, A., Recupero, D. R., & Subrahmanian, V. S. (2007). Sentiment analysis: Adjectives and adverbs are better than adjectives alone. Proceedings of the ICWSM, 3, 1-7.
  • Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Journal of Marketing Research, 49(2), 192-205.
  • Bollegala, D., Weir, D., & Carroll, J. (2012). Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1719-1731.
  • Cawsey, T., & Rowley, J. (2016). Social media brand building strategies in B2B companies. Marketing Intelligence & Planning, 34(6), 754-776.
  • Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media: How online consumer reviews evolve. Journal of interactive marketing, 25(2), 85-94.
  • Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575–600.
  • De Vries, L., Gensler, S., & Leeflang, P. S. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83-91.
  • Duncan, T., & Moriarty, S. (1998). A communication based marketing model for managing relationships. Journal of Marketing, 62(2), 1–13.
  • Emarketer. (2010). Complimentary eMarketer Report: Seven guidelines for achieving ROI from social media. Retrieved from http://www.emarketer.com/blog/index.php/complimentary-emarketer-report-guidelines-achieving-roi-social-media/#qLcSBtK2HzEdrxHA.99
  • Erşahin, B., Aktaş, Ö., Kilinc, D., & Erşahin, M. (2019). A hybrid sentiment analysis method for Turkish. Turkish Journal of Electrical Engineering & Computer Sciences, 27(3), 1780-1793.
  • Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of the LREC, 6, 417-422.
  • 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.
  • Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833-5841.
  • Hatzivassiloglou, V., & Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. Proceedings of the 18th Conference on Computational linguistics, 1, 299-305.
  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168-177.
  • Farquhar, P. H. (1989). Managing brand equity. Marketing Research, 1, 24-33.
  • Kalampokis, E., Karamanou, A., Tambouris, E., & Tarabanis, K. A. (2016). Applying brand equity theory to understand consumer opinion in social media. J. UCS, 22(5), 709-734.
  • Kamps, J., Marx, M., Mokken, R. J., & De Rijke, M. (2004). Using WordNet to measure semantic orientations of adjectives. Proceedings of the LREC, 4, 1115-1118.
  • Karjaluoto, H., Ulkuniemi, P., Huotari, L., Saraniemi, S., & Mäläskä, M. (2015). Analysis of content creation in social media by B2B companies. Journal of Business & Industrial Marketing, 6(30), 761–770
  • Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22.
  • Keller, K. L. (2009). Building strong brands in a modern marketing communications environment. Journal of Marketing Communications, 15(2–3), 139–155.
  • Kim, S. M., & Hovy, E. (2004). Determining the sentiment of opinions. Proceedings of the 20th international conference on Computational Linguistics, 1 – 8.
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.
  • Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.) Thousand Oaks, CA: Sage Publications.
  • Kumar, V., & Mirchandani, R. (2012). Winning with data: Increasing the ROI of social media marketing. MIT Sloan Management Review, 54(1), 55–61.
  • Lai, C. S., Chiu, C. J., Yang, C. F., & Pai, D. C. (2010). The effects of corporate social responsibility on brand performance: The mediating effect of industrial brand equity and corporate reputation. Journal of Business Ethics, 95(3), 457-469.
  • Labrecque, L. I., Zanjani, S. H., & Milne, G. R. (2012). Authenticity in online Communications. In Online consumer behavior: Theory and research in social media, advertising, and e-tail (pp. 133-155). Cham: Springer.
  • Leone, R. P., Rao, V. R., Keller, K. L., Luo, A. M., McAlister, L., & Srivastava, R. (2006). Linking brand equity to customer equity. Journal of Service Research, 9(2), 125-138.
  • Li, S., Xue, Y., Wang, Z., & Zhou, G. (2013). Active learning for cross-domain sentiment classification. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Beijing, 2127-2133
  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
  • Liu, S., Li, F., Li, F., Cheng, X., & Shen, H. (2013). Adaptive co-training SVM for sentiment classification on tweets. Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 10, 2079-2088.
  • Mahajan, V., Rao, V. R., & Srivastava, R. K. (1994). An approach to assess the importance of brand equity in acquisition decisions. Journal of Product Innovation Management, 11(3), 221-235.
  • Meng, X., Wei, F., Liu, X., Zhou, M., Xu, G., & Wang, H. (2012). Cross-lingual mixture model for sentiment classification. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 7, 572-581.
  • Michaelidou, N., Siamagka, N. T., & Christodoulides, G. (2011). Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial Marketing Management, 40, 1153–1159.
  • Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444-456.
  • Moilanen, K., Pulman, S., & Zhang, Y. (2010). Packed feelings and ordered sentiments: Sentiment parsing with quasi-compositional polarity sequencing and compression. Proceedings of the WASSA Workshop at ECAI, 8, 36-43.
  • Mohammad, S., Dunne, C., & Dorr, B. (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2, 599-608.
  • Mueller, K., Garg, S., Nam, J. E., Berg, T., & McDonnell, K. T. (2011). Can computers master the art of communication?: A focus on visual analytics. IEEE Computer Graphics and Applications, 31(3), 14-21.
  • Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016). SemEval-2016 task 4: Sentiment analysis in Twitter. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 5, 1-18.
  • Netzer, Y., Tenenboim-Weinblatt, K., & Shifman, L. (2014). The construction of participation in news websites: A five-dimensional model. Journalism Studies, 15(5), 619-631.
  • Pan, S. J., Ni, X., Sun, J. T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. Proceedings of the 19th international conference on World Wide Web, 4, 751-760.
  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, 7, 271.
  • Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9-28). London: Springer.
  • Rapp, A., Beitelspacher, L. S., Grewal, D., & Hughes, D. E. (2013). Understanding social media effects across seller, retailer, and consumer interactions. Journal of the Academy of Marketing Science, 41(5), 547–566.
  • Spire Inc. (2018). Spire Reports 2018. Prnewswire web sitesinden erişildi: https://www.prnewswire.com/news-releases/spire-reports-2018-results-300750871.html
  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1631-1642.
  • Strapparava, C., & Valitutti, A. (2004). Wordnet affect: an affective extension of wordnet. Proceedings of the LREC, 4, 1083-1086.
  • Strapparava, C., Valitutti, A., & Stock, O. (2006). The affective weight of lexicon. Proceedings of the LREC, 5, 423-426.
  • Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies' Twitter communications. Industrial Marketing Management, 43(5), 873-881.
  • Taylor, D. G., Lewin, J. E., & Strutton, D. (2011). Friends, fans, and followers: Do ads work on social networks? How gender and age shape receptivity. Journal of Advertising Research, 51(1), 258–275.
  • Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198-215.
  • Wan, X. (2012). A comparative study of cross-lingual sentiment classification. Proceedings of the 2012 International Conferences on Web Intelligence and Intelligent Agent Technology, 1, 24-31.
  • Wan, X. (2009). Co-training for cross-lingual sentiment classification. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 1, 235-243.
  • Wiebe, J., Wilson, T., Bruce, R., Bell, M., & Martin, M. (2004). Learning subjective language. Computational Linguistics, 30(3), 277-308.
  • Wiersema, F. (2013). The B2B agenda: The current state of B2B marketing and a look ahead. Industrial Marketing Management, 42(4), 470–488.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Sedat Baştuğ 0000-0002-7121-2882

Vahit Çalışır 0000-0001-6575-8988

Seçil Gülmez 0000-0002-4342-4386

Alpaslan Ateş 0000-0002-0933-2664

Yayımlanma Tarihi 24 Temmuz 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 65

Kaynak Göster

APA Baştuğ, S., Çalışır, V., Gülmez, S., Ateş, A. (2020). Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi(65), 85-106.
AMA Baştuğ S, Çalışır V, Gülmez S, Ateş A. Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi. Temmuz 2020;(65):85-106.
Chicago Baştuğ, Sedat, Vahit Çalışır, Seçil Gülmez, ve Alpaslan Ateş. “Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages”. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, sy. 65 (Temmuz 2020): 85-106.
EndNote Baştuğ S, Çalışır V, Gülmez S, Ateş A (01 Temmuz 2020) Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 65 85–106.
IEEE S. Baştuğ, V. Çalışır, S. Gülmez, ve A. Ateş, “Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, sy. 65, ss. 85–106, Temmuz 2020.
ISNAD Baştuğ, Sedat vd. “Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages”. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 65 (Temmuz 2020), 85-106.
JAMA Baştuğ S, Çalışır V, Gülmez S, Ateş A. Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi. 2020;:85–106.
MLA Baştuğ, Sedat vd. “Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages”. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, sy. 65, 2020, ss. 85-106.
Vancouver Baştuğ S, Çalışır V, Gülmez S, Ateş A. Measuring Port Brand Equity: A Sentiment Analysis on Port Social Media Messages. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi. 2020(65):85-106.

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