Research Article
BibTex RIS Cite

Kargo Firması Müşterilerinin Twitter Gönderilerinin Duygu Analizi

Year 2021, Volume: 18 Issue: 1, 31 - 39, 30.06.2021

Abstract

Son yıllarda lojistik sektörü kapsamında yer alan ve artan ticaret hacmi ile hem sektöre, hem de ekonomiye katkısının öneminin arttığı kargo hizmetlerinde müşterilerin tutum ve davranışlarının belirlenmesi, kargo işletmelerinin rekabet avantajı kazanması açısından bir zorunluluk haline gelmiştir. Özellikle müşterilerin almış oldukları hizmetlerden duydukları memnuniyet durumlarını sosyal medya platformlarından paylaşması işletmelerde müşteri odaklı faaliyetlerin artmasına neden olmuştur. Sosyal medya platformları içerisinde önemli bir yere sahip olan Twitter aracılığıyla bireylerin hizmet aldıkları kargo işletmeleri ile ilgili yorumlarını yoğun olarak paylaştıkları bilinmektedir. Bu çalışmada da doğal dil işleme yöntemleri kullanılarak kargo firma müşterilerinin sosyal medya üzerindeki görüşleri derlenerek firmalar için destek unsuru oluşturulması hedeflenmiştir. Bu amaçla Twitter’da Kasım 2020-Ocak 2021 tarihleri arasında belirlenen bir kargo firması ile ilgili paylaşılan 1138 Twitter gönderisi derlenerek, duygu analizi tekniği ile polarite skoruna göre pozitif, negatif veya nötr olma durumları analiz edilmiştir. Duygu analizi yapılan gönderilerden negatif etiketlenenlerin en yüksek paya sahip olduğu ve genel olarak gönderilerde sık kullanılan kelimelerin “engel”, “uzak” ve “kal” olduğu belirlenmiştir. Araştırmanın sonuçlarının kargo işletmesi yöneticileri açısından önemli bilgiler içermesinin yanı sıra, gelecekte farklı sektörlerde uygulanması açısından değer taşımaktadır.

References

  • Akehurst, G. (2009). User Generated Content: The Use of Blogs For Tourism Organisations and Tourism Consumers. Service Business, 3(1), 51-61.
  • Akın, B. & Şimşek, U. T. G. (2018). Sosyal Medya Analitiği İle Değer Yaratma: Duygu Analizi İle Geleceğe Yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3), 797-811.
  • Akkan, E. (2013). Internet Ortamındaki Kargo Hizmetlerine Yönelik Müşteri Şikayetlerinin İncelenmesi. 2.Ulusal Lojistik ve Tedarik Zinciri Kongresi, Aksaray, Türkiye.
  • Aladwani, A. M. (2015). Facilitators, Characteristics, and Impacts of Twitter Use: Theoretical Analysis and Empirical Illustration. International Journal of Information Management, 35(1), 15-25.
  • Alp, M., Köleoğlu, N., & Çınar, B. (2019). Kargo Firmalarının Müşteri Memnuniyeti ve Firma İtibari Açısından İncelenmesi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (60), 1-13.
  • Arabacı, H., & Yücel, D. (2020). Lojistik Sektörünün Ekonomik Büyümeye Etkisi. Balkan ve Yakın Doğu Sosyal Bilimler Dergisi, 06(4), 78-84.
  • Atmaca, H. E., & Turgut, D. (2015). Kargo Şirketi Seçimine Yönelik Kriterlerin Belirlenmesinde Türkiye Genelinde Bir Saha Araştırması. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(2), 65-79.
  • Bhadane, C., Dalal, H., & Doshi, H. (2015). Sentiment Analysis: Measuring Opinions. Procedia Computer Science, 45, 808-814.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter Mood Predicts the Stock Market. Journal of Computational Science, 2(1), 1-8.
  • Chan, N. L., & Guillet, B. D. (2011). Investigation of Social Media Marketing: How Does the Hotel Industry in Hong Kong Perform in Marketing on Social Media Websites? Journal of Travel & Tourism Marketing, 28, 345-368.
  • Chiu, C., Chiu, N. H., Sung, R. J., & Hsieh, P. Y. (2015). Opinion Mining Of Hotel Customer-Generated Contents in Chinese Weblogs. Current Issues in Tourism, 18(5), 477-495.
  • Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The Power of The “Like” Button: The Impact of Social Media on Box Office. Decision Support Systems, 94, 77-84.
  • Elkin, L. S., Topal, K., & Bebek, G. (2017). Network Based Model of Social Media Big Data Predicts Contagious Disease Diffusion. Information Discovery and Delivery. 45(3), 110-120.
  • Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining. In LREC (Vol. 6, 417-422).
  • Feldman, R., & Sanger, J. (2007). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
  • García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big Data Preprocessing: Methods and Prospects. Big Data Analytics, 1(1), 1-22.
  • Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between Customer Sentiment and Online Customer Ratings for Hotels-An Empirical Analysis. Tourism Management, 61, 43-54.
  • Gligorić, K., Anderson, A., & West, R. (2018). How Constraints Affect Content: The Case of Twitter’s Switch from 140 to 280 Characters. In Proceedings of the International AAAI Conference on Web and Social Media, 12(1), 596-599.
  • Güven, H. (2020). Covid-19 Sürecinde E-Ticaret Sitelerine Yöneltilen Müşteri Şikâyetlerinin İncelenmesi. Electronic Turkish Studies, 15(4), 511-530.
  • He, W., Zha, S., & Li, L. (2013). Social Media Competitive Analysis and Text Mining: A Case Study in The Pizza Industry. International Journal of Information Management, 33(3), 464-472.
  • Hitchcock, L. I., & Young, J. A. (2016). Tweet, Tweet!: Using Live Twitter Chats in Social Work Education. Social Work Education, 35(4), 457-468.
  • Hu, G., Bhargava, P., Fuhrmann, S., Ellinger, S., & Spasojevic, N. (2017). Analyzing Users’ Sentiment towards Popular Consumer Industries and Brands on Twitter. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (381-388).
  • Karayılmazlar, S., Bardak, T., Özkan, A. V. C. I., Kayahan, K., Karayılmazlar, A. S., Çabuk, Y. & İmren, E. (2019). Veri Madenciliği Algoritmalarına Dayalı Olarak Sosyal Medya Üzerinden Mobilya Seçimindeki Yönelimlerin Belirlenmesi: Twitter Örneği. Türkiye Ormancılık Dergisi, 20(4), 447-457.
  • Kirilenko, A. P., & Stepchenkova, S. O. (2014). Public Microblogging on Climate Change: One Year of Twitter Worldwide. Global Environmental Change, 26, 171-182.
  • Krishnan, H., Sudheep, M., & Santhanakrishnan, T. (2017). Sentiment Analysis of Tweets for Inferring Popularity of Mobile Phones. International Journal of Computer Applications, 157(2), 1-3.
  • Kumar, A., & Sebastian, T. M. (2012). Sentiment Analysis on Twitter. International Journal of Computer Science Issues (IJCSI), 9(4), 372-378.
  • Kumari, M. V. & Prajna, B. (2021). Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 3597-3603.
  • Leek, S., Canning, L., & Houghton, D. (2016). Revisiting the Task Media Fit Model in The Era of Web 2.0: Twitter Use and Interaction in The Healthcare Sector. Industrial Marketing Management, 54, 25-32.
  • Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social Media in Tourism and Hospitality: A Literature Review. Journal of Travel & Tourism Marketing, 30(1-2), 3-22.
  • Olaleye, S. A., Sanusi, I. T., & Salo, J. (2018). Sentiment Analysis of Social Commerce: A Harbinger of Online Reputation Management. International Journal of Electronic Business, 14(2), 85-102.
  • Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis, Foundations and Trends (R) in Information Retrieval. 2, 1-135.
  • Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding Customers' Hotel Revisiting Behaviour: A Sentiment Analysis of Online Feedback Reviews. Current Issues in Tourism, 23(5), 605-611.
  • Pawar, K. K., Shrishrimal, P. P., & Deshmukh, R. R. (2015). Twitter Sentiment Analysis: A Review. International Journal of Scientific & Engineering Research, 6(4), 957-964.
  • Proserpio, D., & Zervas, G. (2017). Online reputation management: Estimating the impact of management responses on consumer reviews. Marketing Science, 36(5), 645-665.
  • Qadir, A. M., & Cooper, P. (2020). GPS-based Mobile Cross-platform Cargo Tracking System with Web-based Application. In 2020 8th International Symposium on Digital Forensics and Security (ISDFS) (1-7).
  • Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press.
  • Rane, A., & Kumar, A. (2018). Sentiment classification System of Twitter Data for US Airline Service Analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, 769-773).
  • Seker, S. E., & Al-Naami, K. (2013). Sentimental Analysis on Turkish Blogs via Ensemble Classifier. In Proc. the 2013 International Conference on Data Mining (pp. 10-16).
  • Sinha, S., Dyer, C., Gimpel, K., & Smith, N. A. (2013). Predicting the NFL Using Twitter. ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics.
  • Song, J. E., & Jang, W. (2013). Developing the Korean Wave through Encouraging the Participation of Youtube Users: The Case Study of The Korean Wave Youth Fans in Hong Kong. The Journal of the Korea Contents Association, 13(4), 155-169.
  • Sreeja, I., Sunny, J. V., & Jatian, L. (2020). Twitter Sentiment Analysis on Airline Tweets in India Using R Language. In Journal of Physics: Conference Series, 1427(1), p.012003, IOP Publishing.
  • Taecharungroj, V. (2017). Starbucks’ Marketing Communications Strategy on Twitter. Journal of Marketing Communications, 23(6), 552-571.
  • Trivedi, S. K., & Singh, A. (2021). Twitter Sentiment Analysis of App Based Online Food Delivery Companies. Global Knowledge, Memory and Communication. DOI 10.1108/GKMC-04-2020-0056.
  • Wang, X., Wei, F., Liu, X., Zhou, M., & Zhang, M. (2011). Topic Sentiment Analysis in Twitter: A Graph-Based Hashtag Sentiment Classification Approach. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 1031-1040.
  • Wang, J., Gu, Q., & Wang, G. (2013). Potential Power and Problems in Sentiment Mining of Social Media. International Journal of Strategic Decision Sciences (IJSDS), 4(2), 16-26.
  • Weng, J., Lim, E. P., He, Q., & Leung, C. W. K. (2010). What Do People Want in Microblogs? Measuring Interestingness of Hashtags in Twitter. In 2010 IEEE International Conference on Data Mining, 1121-1126.
  • Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, 347-354.
  • 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, 51-65.
  • Zulkarnain, Z., Surjandari, I., & Wayasti, R. A. (2018). Sentiment Analysis for Mining Customer Opinion on Twitter: A Case Study of Ride-Hailing Service Provider. In 2018 5th International Conference on Information Science and Control Engineering (ICISCE), 512-516.
Year 2021, Volume: 18 Issue: 1, 31 - 39, 30.06.2021

Abstract

References

  • Akehurst, G. (2009). User Generated Content: The Use of Blogs For Tourism Organisations and Tourism Consumers. Service Business, 3(1), 51-61.
  • Akın, B. & Şimşek, U. T. G. (2018). Sosyal Medya Analitiği İle Değer Yaratma: Duygu Analizi İle Geleceğe Yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3), 797-811.
  • Akkan, E. (2013). Internet Ortamındaki Kargo Hizmetlerine Yönelik Müşteri Şikayetlerinin İncelenmesi. 2.Ulusal Lojistik ve Tedarik Zinciri Kongresi, Aksaray, Türkiye.
  • Aladwani, A. M. (2015). Facilitators, Characteristics, and Impacts of Twitter Use: Theoretical Analysis and Empirical Illustration. International Journal of Information Management, 35(1), 15-25.
  • Alp, M., Köleoğlu, N., & Çınar, B. (2019). Kargo Firmalarının Müşteri Memnuniyeti ve Firma İtibari Açısından İncelenmesi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (60), 1-13.
  • Arabacı, H., & Yücel, D. (2020). Lojistik Sektörünün Ekonomik Büyümeye Etkisi. Balkan ve Yakın Doğu Sosyal Bilimler Dergisi, 06(4), 78-84.
  • Atmaca, H. E., & Turgut, D. (2015). Kargo Şirketi Seçimine Yönelik Kriterlerin Belirlenmesinde Türkiye Genelinde Bir Saha Araştırması. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(2), 65-79.
  • Bhadane, C., Dalal, H., & Doshi, H. (2015). Sentiment Analysis: Measuring Opinions. Procedia Computer Science, 45, 808-814.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter Mood Predicts the Stock Market. Journal of Computational Science, 2(1), 1-8.
  • Chan, N. L., & Guillet, B. D. (2011). Investigation of Social Media Marketing: How Does the Hotel Industry in Hong Kong Perform in Marketing on Social Media Websites? Journal of Travel & Tourism Marketing, 28, 345-368.
  • Chiu, C., Chiu, N. H., Sung, R. J., & Hsieh, P. Y. (2015). Opinion Mining Of Hotel Customer-Generated Contents in Chinese Weblogs. Current Issues in Tourism, 18(5), 477-495.
  • Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The Power of The “Like” Button: The Impact of Social Media on Box Office. Decision Support Systems, 94, 77-84.
  • Elkin, L. S., Topal, K., & Bebek, G. (2017). Network Based Model of Social Media Big Data Predicts Contagious Disease Diffusion. Information Discovery and Delivery. 45(3), 110-120.
  • Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining. In LREC (Vol. 6, 417-422).
  • Feldman, R., & Sanger, J. (2007). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
  • García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big Data Preprocessing: Methods and Prospects. Big Data Analytics, 1(1), 1-22.
  • Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between Customer Sentiment and Online Customer Ratings for Hotels-An Empirical Analysis. Tourism Management, 61, 43-54.
  • Gligorić, K., Anderson, A., & West, R. (2018). How Constraints Affect Content: The Case of Twitter’s Switch from 140 to 280 Characters. In Proceedings of the International AAAI Conference on Web and Social Media, 12(1), 596-599.
  • Güven, H. (2020). Covid-19 Sürecinde E-Ticaret Sitelerine Yöneltilen Müşteri Şikâyetlerinin İncelenmesi. Electronic Turkish Studies, 15(4), 511-530.
  • He, W., Zha, S., & Li, L. (2013). Social Media Competitive Analysis and Text Mining: A Case Study in The Pizza Industry. International Journal of Information Management, 33(3), 464-472.
  • Hitchcock, L. I., & Young, J. A. (2016). Tweet, Tweet!: Using Live Twitter Chats in Social Work Education. Social Work Education, 35(4), 457-468.
  • Hu, G., Bhargava, P., Fuhrmann, S., Ellinger, S., & Spasojevic, N. (2017). Analyzing Users’ Sentiment towards Popular Consumer Industries and Brands on Twitter. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (381-388).
  • Karayılmazlar, S., Bardak, T., Özkan, A. V. C. I., Kayahan, K., Karayılmazlar, A. S., Çabuk, Y. & İmren, E. (2019). Veri Madenciliği Algoritmalarına Dayalı Olarak Sosyal Medya Üzerinden Mobilya Seçimindeki Yönelimlerin Belirlenmesi: Twitter Örneği. Türkiye Ormancılık Dergisi, 20(4), 447-457.
  • Kirilenko, A. P., & Stepchenkova, S. O. (2014). Public Microblogging on Climate Change: One Year of Twitter Worldwide. Global Environmental Change, 26, 171-182.
  • Krishnan, H., Sudheep, M., & Santhanakrishnan, T. (2017). Sentiment Analysis of Tweets for Inferring Popularity of Mobile Phones. International Journal of Computer Applications, 157(2), 1-3.
  • Kumar, A., & Sebastian, T. M. (2012). Sentiment Analysis on Twitter. International Journal of Computer Science Issues (IJCSI), 9(4), 372-378.
  • Kumari, M. V. & Prajna, B. (2021). Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 3597-3603.
  • Leek, S., Canning, L., & Houghton, D. (2016). Revisiting the Task Media Fit Model in The Era of Web 2.0: Twitter Use and Interaction in The Healthcare Sector. Industrial Marketing Management, 54, 25-32.
  • Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social Media in Tourism and Hospitality: A Literature Review. Journal of Travel & Tourism Marketing, 30(1-2), 3-22.
  • Olaleye, S. A., Sanusi, I. T., & Salo, J. (2018). Sentiment Analysis of Social Commerce: A Harbinger of Online Reputation Management. International Journal of Electronic Business, 14(2), 85-102.
  • Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis, Foundations and Trends (R) in Information Retrieval. 2, 1-135.
  • Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding Customers' Hotel Revisiting Behaviour: A Sentiment Analysis of Online Feedback Reviews. Current Issues in Tourism, 23(5), 605-611.
  • Pawar, K. K., Shrishrimal, P. P., & Deshmukh, R. R. (2015). Twitter Sentiment Analysis: A Review. International Journal of Scientific & Engineering Research, 6(4), 957-964.
  • Proserpio, D., & Zervas, G. (2017). Online reputation management: Estimating the impact of management responses on consumer reviews. Marketing Science, 36(5), 645-665.
  • Qadir, A. M., & Cooper, P. (2020). GPS-based Mobile Cross-platform Cargo Tracking System with Web-based Application. In 2020 8th International Symposium on Digital Forensics and Security (ISDFS) (1-7).
  • Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press.
  • Rane, A., & Kumar, A. (2018). Sentiment classification System of Twitter Data for US Airline Service Analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, 769-773).
  • Seker, S. E., & Al-Naami, K. (2013). Sentimental Analysis on Turkish Blogs via Ensemble Classifier. In Proc. the 2013 International Conference on Data Mining (pp. 10-16).
  • Sinha, S., Dyer, C., Gimpel, K., & Smith, N. A. (2013). Predicting the NFL Using Twitter. ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics.
  • Song, J. E., & Jang, W. (2013). Developing the Korean Wave through Encouraging the Participation of Youtube Users: The Case Study of The Korean Wave Youth Fans in Hong Kong. The Journal of the Korea Contents Association, 13(4), 155-169.
  • Sreeja, I., Sunny, J. V., & Jatian, L. (2020). Twitter Sentiment Analysis on Airline Tweets in India Using R Language. In Journal of Physics: Conference Series, 1427(1), p.012003, IOP Publishing.
  • Taecharungroj, V. (2017). Starbucks’ Marketing Communications Strategy on Twitter. Journal of Marketing Communications, 23(6), 552-571.
  • Trivedi, S. K., & Singh, A. (2021). Twitter Sentiment Analysis of App Based Online Food Delivery Companies. Global Knowledge, Memory and Communication. DOI 10.1108/GKMC-04-2020-0056.
  • Wang, X., Wei, F., Liu, X., Zhou, M., & Zhang, M. (2011). Topic Sentiment Analysis in Twitter: A Graph-Based Hashtag Sentiment Classification Approach. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 1031-1040.
  • Wang, J., Gu, Q., & Wang, G. (2013). Potential Power and Problems in Sentiment Mining of Social Media. International Journal of Strategic Decision Sciences (IJSDS), 4(2), 16-26.
  • Weng, J., Lim, E. P., He, Q., & Leung, C. W. K. (2010). What Do People Want in Microblogs? Measuring Interestingness of Hashtags in Twitter. In 2010 IEEE International Conference on Data Mining, 1121-1126.
  • Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, 347-354.
  • 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, 51-65.
  • Zulkarnain, Z., Surjandari, I., & Wayasti, R. A. (2018). Sentiment Analysis for Mining Customer Opinion on Twitter: A Case Study of Ride-Hailing Service Provider. In 2018 5th International Conference on Information Science and Control Engineering (ICISCE), 512-516.
There are 49 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Kalender Özcan Atılgan 0000-0003-1482-4505

Hakan Yoğurtcu 0000-0002-4292-5621

Early Pub Date June 30, 2021
Publication Date June 30, 2021
Published in Issue Year 2021 Volume: 18 Issue: 1

Cite

APA Atılgan, K. Ö., & Yoğurtcu, H. (2021). Kargo Firması Müşterilerinin Twitter Gönderilerinin Duygu Analizi. Çağ Üniversitesi Sosyal Bilimler Dergisi, 18(1), 31-39.