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SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES

Year 2020, Volume 6, Issue 1, 26 - 41, 15.06.2020

Abstract

Along with technological developments, concepts such as big data, big data analytics, social media, and social media analytics have been included in the agenda of marketing and other management sciences. It is known that among the biggest obstacles encountered in applying new methods, which are generally called business analytics, is the lack of knowledge and experience. In this study, an exemplary application that could be useful to academics and business managers who want to work in social media analytics applications, a subdivision of business analytics, has been implemented. Within the scope of the study, 6667 Twitter messages shared between 23.03.2018 - 02.04.2018 in English about Turkish Airlines (THY) are fetched and recorded into a database using open source R programming language. Messages were classified as positive and negative by applying sentiment analysis implementation of syuzhet R package on these messages. Daily positive and negative message counts are computed and the changes in counts are interpreted. A total of 50 randomly selected samples’ Twitter account names, negative/positive message counts, locations, latitude and longitude information, and profile photos are downloaded from the Twitter server. The samples are then viewed on the map via the Google Maps API. It is examined how these applications made within the scope of Data Visualization can be used in administrative processes.

References

  • Ahuja, V. and Shakeel, M. (2017). Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds. Procedia Computer Science, 122, pp.17–24.
  • Armstrong, G. and Kotler, P. (2017). Marketing: An Introduction. 13th ed. Pearson.
  • Arun, K., Srinagesh, A. and Ramesh, M. (2017). Twitter Sentiment Analysis on Demonetization tweets in India Using R language. International Journal of Computer Engineering In Research Trends, 4(6), pp.252–258.
  • Birjali, M., Beni-Hssane, A. and Erritali, M. (2017). Analyzing Social Media through Big Data using InfoSphere BigInsights and Apache Flume. Procedia Computer Science, 113, pp.280–285. [online]. Available from: https://www.sciencedirect.com/science/article/pii/S1877050917317088.
  • Bone, P.F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), pp.213–223.
  • Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems, 109(2), pp.155–172.
  • Cajachahua, L. and Burga, I. (2017). Sentiments and Opinions From Twitter About Peruvian Touristic Places Using Correspondence Analysis. CEUR Workshop Proceedings, 2029, pp.178–189.
  • Chang, Y.-C., Ku, C.-H. and Chen, C.-H. (2017). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management.
  • Charan, A. (2015). Marketing analytics: A Practitioner’s Guide to Marketing Analytics and Research Methods. World Scientific Publishing Company.
  • Cuevas, J.M. (2018). The transformation of professional selling: Implications for leading the modern sales organization. Industrial Marketing Management, 69, pp.198–208.
  • Das, S.R., Kim, S. and Kothari, B. (2017). Zero-Revelation RegTech: Detecting Risk through Linguistic Analysis of Corporate Emails and News. SSRN Electronic Journal, pp.0–32.
  • Duan, W., Gu, B. and Whinston, A.B. (2008). The dynamics of online word-of-mouth and product sales-An empirical investigation of the movie industry. Journal of Retailing, 84(2), pp.233–242.
  • Elbashir, M.Z., Collier, P.A. and Davern, M.J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), pp.135–153.
  • Evans, J.R. (2016). Business Analytics. 2nd ed. Boston: Pearson Education.
  • Gao, J. et al. (2017). A multiscale theory for the dynamical evolution of sentiment in novels. IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing.
  • Germann, F., Lilien, G.L. and Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), pp.114–128.
  • Grubert, E. and Algee-Hewitt, M. (2017). Villainous or valiant? Depictions of oil and coal in American fiction and nonfiction narratives. Energy Research and Social Science, 31(October 2016), pp.100–110.
  • Guesalaga, R. (2016). The use of social media in sales: Individual and organizational antecedents, and the role of customer engagement in social media. Industrial Marketing Management, 54, pp.71–79.
  • Hari, S., Kolagani, D. and Student, M.B.A. (2017). Issues in Information Systems IDENTIFYING TRENDING SENTIMENTS IN THE 2016 U . S . PRESIDENTIAL Issues in Information Systems. , 18(2), pp.80–86.
  • Haripriya, A. and Kumari, S. (2017). Real time analysis of top trending event on Twitter: Lexicon based approach. 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, pp.3–6.
  • Hoffmann, T. (2018). “ Too many Americans are trapped in fear , violence and poverty ” : a psychology-informed sentiment analysis of campaign speeches from the 2016 US Presidential Election. Linguistics Vanguard, pp.1–9.
  • Holsapple, C.W., Hsiao, S.-H. and Pakath, R. (2018). Business social media analytics: Characterization and conceptual framework. Decision Support Systems.
  • International Data Corporation (IDC). (2014). The digital universe of opportunities. [online]. Available from: https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm [Accessed February 2, 2017].
  • Itani, O.S., Agnihotri, R. and Dingus, R. (2017). Social media use in {B2b} sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66, pp.64–79.
  • Jockers, M. (2017). Package ‘ syuzhet .’ [online]. Available from: https://github.com/mjockers/syuzhet.
  • Kale, G.Ö. (2016). Marka İletişiminde Instagram Kullanımı. The Turkish Online Journal of Design, Art and Communication, 6(2), pp.119–127.
  • Ketter, E. (2016). Destination image restoration on facebook: The case study of Nepal’s Gurkha Earthquake. Journal of Hospitality and Tourism Management, 28, pp.66–72.
  • Kim, E., Sung, Y. and Kang, H. (2014). Brand followers’ retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth. Computers in Human Behavior, 37, pp.18–25.
  • Kumar, V. (2007). Customer Lifetime Value – The Path to Profitability. Foundations and Trends® in Marketing, 2(1), pp.1–96.
  • Laudon, K.C. and Traver, C.G. (2017). E-Commerce 2017. 13th ed. Pearson Education Limited.
  • Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons, 61(2), pp.199–210.
  • Madigan, R. and Bliss, G. (2016). HHS Public Access. , 12(5), pp.485–493.
  • Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, pp.46–60.
  • Melancon, J.P. and Dalakas, V. (2018). Consumer social voice in the age of social media: Segmentation profiles and relationship marketing strategies. Business Horizons, 61(1), pp.157–167.
  • Misirlis, N. and Vlachopoulou, M. (2018). Social media metrics and analytics in marketing – S3M: A mapping literature review. International Journal of Information Management, 38(1), pp.270–276.
  • Mohammad, S.M. and Turney, P.D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3), pp.436–465.
  • Öztürk, N. and Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), pp.136–147.
  • Pabreja, K. (2017). GST sentiment analysis using twitter data. , 3(7), pp.660–662.
  • Ragupathy, R. and Maguluri, L.P. (2018). Comparative analysis of machine learning algorithms on social media test. , 7, pp.284–290.
  • Rubin, E. and Rubin, A. (2013). The impact of Business Intelligence systems on stock return volatility. Information & Management, 50(2–3), pp.67–75.
  • Saif, H. et al. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52(1), pp.5–19.
  • Salas, J. (2018). Generating music from literature using topic extraction and sentiment analysis. , pp.15–18.
  • Salo, J. (2017). Social media research in the industrial marketing field: Review of literature and future research directions. Industrial Marketing Management, 66, pp.115–129.
  • Shmueli, G. et al. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
  • Singla, Z., Randhawa, S. and Jain, S. (2017). Sentiment Analysis of Customer Product Reviews Using Machine Learning.
  • Singla, Z., Randhawa, S. and Jain, S. (2017). Sentiment Analysis of Customer Product Reviews Using Machine Learning. In 2017 International Conference on Intelligent Computing and Control (I2C2). Tamil Nadu, India.
  • Sitta, D., Faulkner, M. and Stern, P. (2018). What can the brand manager expect from Facebook? Australasian Marketing Journal, pp.1–6.
  • Subbulakshmi, T. and Raja, R.R. (2016). An Ensemble Approach For Sentiment Classification: Voting For Classes And Against Them. ICTACT Journal on Soft Computing, 6956(July), pp.1281–1286.
  • Suoniemi, S. et al. (2014). Working Paper: Use Of Big Data Analytics for Customer Relatıonship Management: Point of Parity or Source of Competitive Advantage? [online]. Available from: https://www.researchgate.net/profile/Samppa_Suoniemi/publication/321589031_Big_Data-Driven_Marketing_An_Abstract/links/5ad0ab78458515c60f4e4a0f/Big-Data-Driven-Marketing-An-Abstract.pdf.
  • Syam, N. and Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, pp.135–146.
  • Tajvidi, R. and Karami, A. (2017). The effect of social media on firm performance. Computers in Human Behavior, p.
  • Thinyane, H. and Thinyane, M. (2017). The use of sentiment analysis and topic modelling to understand online communicative ecologies in MobiSAM. 2017 IST-Africa Week Conference, IST-Africa 2017, pp.1–8.
  • Trkman, P. et al. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), pp.318–327.
  • Vaughan, R.J. (2017). Examining the Data Analytics Skill Gap in Mid-Level Marketing Professionals , Driven by the Continuing Exponential Growth of Big Data. , 5(3).
  • Venkatesan, R., Farris, P. and Wilcox, R.T. (2014). Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands on Learning. Pearson Education.
  • Wang, W.Y.C., Pauleen, D.J. and Zhang, T. (2016). How social media applications affect {B2B} communication and improve business performance in {SMEs}. Industrial Marketing Management, 54, pp.4–14.
  • Winston, W.L. and Goldberg, J.B. (2004). Operations research : applications and algorithms. Thomson/Brooks/Cole.
  • Xu, Z., Frankwick, G.L. and Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), pp.1562–1566.
  • Zehe, A. et al. (2016). Prediction of happy endings in German novels based on sentiment information. CEUR Workshop Proceedings, 1646(Section 6), pp.9–16.

MARKA İMAJ TAKİBİ İÇİN SOSYAL MEDYA ANALİTİĞİ: TÜRK HAVA YOLLARI İÇİN BİR VAKA ÇALIŞMASI

Year 2020, Volume 6, Issue 1, 26 - 41, 15.06.2020

Abstract

Teknolojik gelişmeler ile birlikte büyük veri, büyük veri analitiği, sosyal medya, sosyal medya analitiği gibi kavramlar pazarlama ve diğer yönetim bilimlerinin gündemine dahil olmuştur. Genelde iş analitiği olarak adlandırılan yeni yöntemlerin uygulanması sırasında karşılaşılan en büyük engeller arasında, bilgi ve tecrübe eksikliğinin olduğu bilinmektedir. Bu çalışmada, iş analitiğinin bir alt dalı olan sosyal medya analitiği uygulamalarında çalışmak isteyen akademisyenlere ve işletme yöneticilerine yararı olabilecek örnek bir uygulama gerçekleştirilmiştir. Çalışma kapsamında, ücretsiz (açık kaynak kodlu) R programlama dili ile hazırlanan yazılımla, Türk Hava Yolları (THY) hakkında Twitter’da İngilizce olarak 23.03.2018 – 02.04.2018 tarihleri arasında paylaşılmış 6667 mesaj dakikalar içerisinde tespit edilip veri tabanına kaydedilmiştir. Bu mesajlara duygu (sentiment) analizi algoritması, syuzhet R paketi uygulanarak, mesajlar pozitif ve negatif olarak sınıflandırılmıştır. Mesajların günlere göre pozitif ve negatif sayıları tespit edilip, değişimler yorumlanmıştır. Bu mesajları paylaşan kişilerden, rastlantısal seçilen toplam 50 örneklemin, Twitter hesap adları, negatif/pozitif mesaj sayıları, konumları, enlem ve boylam bilgileri, profil fotoğrafları Twitter sunucusundan indirilip, Google Haritalar API uygulaması kullanılarak, harita üzerinde bu kişilerin görülmesi sağlanmıştır. Verilerin görselleştirilmesi (Data Visualization) kapsamında yapılan bu uygulamaların, yönetimsel süreçlerde nasıl kullanılabileceği incelenmiştir.

References

  • Ahuja, V. and Shakeel, M. (2017). Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds. Procedia Computer Science, 122, pp.17–24.
  • Armstrong, G. and Kotler, P. (2017). Marketing: An Introduction. 13th ed. Pearson.
  • Arun, K., Srinagesh, A. and Ramesh, M. (2017). Twitter Sentiment Analysis on Demonetization tweets in India Using R language. International Journal of Computer Engineering In Research Trends, 4(6), pp.252–258.
  • Birjali, M., Beni-Hssane, A. and Erritali, M. (2017). Analyzing Social Media through Big Data using InfoSphere BigInsights and Apache Flume. Procedia Computer Science, 113, pp.280–285. [online]. Available from: https://www.sciencedirect.com/science/article/pii/S1877050917317088.
  • Bone, P.F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), pp.213–223.
  • Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems, 109(2), pp.155–172.
  • Cajachahua, L. and Burga, I. (2017). Sentiments and Opinions From Twitter About Peruvian Touristic Places Using Correspondence Analysis. CEUR Workshop Proceedings, 2029, pp.178–189.
  • Chang, Y.-C., Ku, C.-H. and Chen, C.-H. (2017). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management.
  • Charan, A. (2015). Marketing analytics: A Practitioner’s Guide to Marketing Analytics and Research Methods. World Scientific Publishing Company.
  • Cuevas, J.M. (2018). The transformation of professional selling: Implications for leading the modern sales organization. Industrial Marketing Management, 69, pp.198–208.
  • Das, S.R., Kim, S. and Kothari, B. (2017). Zero-Revelation RegTech: Detecting Risk through Linguistic Analysis of Corporate Emails and News. SSRN Electronic Journal, pp.0–32.
  • Duan, W., Gu, B. and Whinston, A.B. (2008). The dynamics of online word-of-mouth and product sales-An empirical investigation of the movie industry. Journal of Retailing, 84(2), pp.233–242.
  • Elbashir, M.Z., Collier, P.A. and Davern, M.J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), pp.135–153.
  • Evans, J.R. (2016). Business Analytics. 2nd ed. Boston: Pearson Education.
  • Gao, J. et al. (2017). A multiscale theory for the dynamical evolution of sentiment in novels. IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing.
  • Germann, F., Lilien, G.L. and Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), pp.114–128.
  • Grubert, E. and Algee-Hewitt, M. (2017). Villainous or valiant? Depictions of oil and coal in American fiction and nonfiction narratives. Energy Research and Social Science, 31(October 2016), pp.100–110.
  • Guesalaga, R. (2016). The use of social media in sales: Individual and organizational antecedents, and the role of customer engagement in social media. Industrial Marketing Management, 54, pp.71–79.
  • Hari, S., Kolagani, D. and Student, M.B.A. (2017). Issues in Information Systems IDENTIFYING TRENDING SENTIMENTS IN THE 2016 U . S . PRESIDENTIAL Issues in Information Systems. , 18(2), pp.80–86.
  • Haripriya, A. and Kumari, S. (2017). Real time analysis of top trending event on Twitter: Lexicon based approach. 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, pp.3–6.
  • Hoffmann, T. (2018). “ Too many Americans are trapped in fear , violence and poverty ” : a psychology-informed sentiment analysis of campaign speeches from the 2016 US Presidential Election. Linguistics Vanguard, pp.1–9.
  • Holsapple, C.W., Hsiao, S.-H. and Pakath, R. (2018). Business social media analytics: Characterization and conceptual framework. Decision Support Systems.
  • International Data Corporation (IDC). (2014). The digital universe of opportunities. [online]. Available from: https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm [Accessed February 2, 2017].
  • Itani, O.S., Agnihotri, R. and Dingus, R. (2017). Social media use in {B2b} sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66, pp.64–79.
  • Jockers, M. (2017). Package ‘ syuzhet .’ [online]. Available from: https://github.com/mjockers/syuzhet.
  • Kale, G.Ö. (2016). Marka İletişiminde Instagram Kullanımı. The Turkish Online Journal of Design, Art and Communication, 6(2), pp.119–127.
  • Ketter, E. (2016). Destination image restoration on facebook: The case study of Nepal’s Gurkha Earthquake. Journal of Hospitality and Tourism Management, 28, pp.66–72.
  • Kim, E., Sung, Y. and Kang, H. (2014). Brand followers’ retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth. Computers in Human Behavior, 37, pp.18–25.
  • Kumar, V. (2007). Customer Lifetime Value – The Path to Profitability. Foundations and Trends® in Marketing, 2(1), pp.1–96.
  • Laudon, K.C. and Traver, C.G. (2017). E-Commerce 2017. 13th ed. Pearson Education Limited.
  • Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons, 61(2), pp.199–210.
  • Madigan, R. and Bliss, G. (2016). HHS Public Access. , 12(5), pp.485–493.
  • Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, pp.46–60.
  • Melancon, J.P. and Dalakas, V. (2018). Consumer social voice in the age of social media: Segmentation profiles and relationship marketing strategies. Business Horizons, 61(1), pp.157–167.
  • Misirlis, N. and Vlachopoulou, M. (2018). Social media metrics and analytics in marketing – S3M: A mapping literature review. International Journal of Information Management, 38(1), pp.270–276.
  • Mohammad, S.M. and Turney, P.D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3), pp.436–465.
  • Öztürk, N. and Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), pp.136–147.
  • Pabreja, K. (2017). GST sentiment analysis using twitter data. , 3(7), pp.660–662.
  • Ragupathy, R. and Maguluri, L.P. (2018). Comparative analysis of machine learning algorithms on social media test. , 7, pp.284–290.
  • Rubin, E. and Rubin, A. (2013). The impact of Business Intelligence systems on stock return volatility. Information & Management, 50(2–3), pp.67–75.
  • Saif, H. et al. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52(1), pp.5–19.
  • Salas, J. (2018). Generating music from literature using topic extraction and sentiment analysis. , pp.15–18.
  • Salo, J. (2017). Social media research in the industrial marketing field: Review of literature and future research directions. Industrial Marketing Management, 66, pp.115–129.
  • Shmueli, G. et al. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
  • Singla, Z., Randhawa, S. and Jain, S. (2017). Sentiment Analysis of Customer Product Reviews Using Machine Learning.
  • Singla, Z., Randhawa, S. and Jain, S. (2017). Sentiment Analysis of Customer Product Reviews Using Machine Learning. In 2017 International Conference on Intelligent Computing and Control (I2C2). Tamil Nadu, India.
  • Sitta, D., Faulkner, M. and Stern, P. (2018). What can the brand manager expect from Facebook? Australasian Marketing Journal, pp.1–6.
  • Subbulakshmi, T. and Raja, R.R. (2016). An Ensemble Approach For Sentiment Classification: Voting For Classes And Against Them. ICTACT Journal on Soft Computing, 6956(July), pp.1281–1286.
  • Suoniemi, S. et al. (2014). Working Paper: Use Of Big Data Analytics for Customer Relatıonship Management: Point of Parity or Source of Competitive Advantage? [online]. Available from: https://www.researchgate.net/profile/Samppa_Suoniemi/publication/321589031_Big_Data-Driven_Marketing_An_Abstract/links/5ad0ab78458515c60f4e4a0f/Big-Data-Driven-Marketing-An-Abstract.pdf.
  • Syam, N. and Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, pp.135–146.
  • Tajvidi, R. and Karami, A. (2017). The effect of social media on firm performance. Computers in Human Behavior, p.
  • Thinyane, H. and Thinyane, M. (2017). The use of sentiment analysis and topic modelling to understand online communicative ecologies in MobiSAM. 2017 IST-Africa Week Conference, IST-Africa 2017, pp.1–8.
  • Trkman, P. et al. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), pp.318–327.
  • Vaughan, R.J. (2017). Examining the Data Analytics Skill Gap in Mid-Level Marketing Professionals , Driven by the Continuing Exponential Growth of Big Data. , 5(3).
  • Venkatesan, R., Farris, P. and Wilcox, R.T. (2014). Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands on Learning. Pearson Education.
  • Wang, W.Y.C., Pauleen, D.J. and Zhang, T. (2016). How social media applications affect {B2B} communication and improve business performance in {SMEs}. Industrial Marketing Management, 54, pp.4–14.
  • Winston, W.L. and Goldberg, J.B. (2004). Operations research : applications and algorithms. Thomson/Brooks/Cole.
  • Xu, Z., Frankwick, G.L. and Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), pp.1562–1566.
  • Zehe, A. et al. (2016). Prediction of happy endings in German novels based on sentiment information. CEUR Workshop Proceedings, 1646(Section 6), pp.9–16.

Details

Primary Language English
Subjects Social
Journal Section Articles
Authors

İbrahim SABUNCU> (Primary Author)
YALOVA UNIVERSITY
0000-0001-8625-9256
Türkiye


Mahir ATMİS>
YALOVA UNIVERSITY
Türkiye

Publication Date June 15, 2020
Published in Issue Year 2020, Volume 6, Issue 1

Cite

Bibtex @research article { ybs674295, journal = {Yönetim Bilişim Sistemleri Dergisi}, issn = {2630-550X}, eissn = {2630-550X}, address = {}, publisher = {Vahap TECİM}, year = {2020}, volume = {6}, number = {1}, pages = {26 - 41}, title = {SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES}, key = {cite}, author = {Sabuncu, İbrahim and Atmis, Mahir} }
APA Sabuncu, İ. & Atmis, M. (2020). SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES . Yönetim Bilişim Sistemleri Dergisi , 6 (1) , 26-41 . Retrieved from https://dergipark.org.tr/en/pub/ybs/issue/54333/674295
MLA Sabuncu, İ. , Atmis, M. "SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES" . Yönetim Bilişim Sistemleri Dergisi 6 (2020 ): 26-41 <https://dergipark.org.tr/en/pub/ybs/issue/54333/674295>
Chicago Sabuncu, İ. , Atmis, M. "SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES". Yönetim Bilişim Sistemleri Dergisi 6 (2020 ): 26-41
RIS TY - JOUR T1 - SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES AU - İbrahimSabuncu, MahirAtmis Y1 - 2020 PY - 2020 N1 - DO - T2 - Yönetim Bilişim Sistemleri Dergisi JF - Journal JO - JOR SP - 26 EP - 41 VL - 6 IS - 1 SN - 2630-550X-2630-550X M3 - UR - Y2 - 2020 ER -
EndNote %0 Yönetim Bilişim Sistemleri Dergisi SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES %A İbrahim Sabuncu , Mahir Atmis %T SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES %D 2020 %J Yönetim Bilişim Sistemleri Dergisi %P 2630-550X-2630-550X %V 6 %N 1 %R %U
ISNAD Sabuncu, İbrahim , Atmis, Mahir . "SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES". Yönetim Bilişim Sistemleri Dergisi 6 / 1 (June 2020): 26-41 .
AMA Sabuncu İ. , Atmis M. SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES. Yönetim Bilişim Sistemleri Dergisi. 2020; 6(1): 26-41.
Vancouver Sabuncu İ. , Atmis M. SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES. Yönetim Bilişim Sistemleri Dergisi. 2020; 6(1): 26-41.
IEEE İ. Sabuncu and M. Atmis , "SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES", Yönetim Bilişim Sistemleri Dergisi, vol. 6, no. 1, pp. 26-41, Jun. 2020