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TWİTTER KULLANICILARININ HAVAYOLU PAZARINA YÖNELİK DUYGU KUTUPLARININ BELİRLENMESİ: BİR FİKİR MADENCİLİĞİ ÖRNEĞİ

Year 2016, Volume: 2 Issue: 1, 684 - 691, 01.06.2016
https://doi.org/10.17261/Pressacademia.2016118690

Abstract

Her sektörde olduğu gibi havayolu sektöründe de halihazır ve potansiyel müşterilerin satın alma öncesi ve sonrası fikir ve duygularının tespit edilmesi, havayolu firmalarının gelecekte sunacağı hizmetleri de şekillendirmektedir. Bu çalışmada, Twitter kullanıcılarının havayolu ulaşımı ile ilgili yorumları derlenerek duygu analizi çalışması yapılmıştır. Kullanıcı yorumları, birçok sosyal medya uygulamasında olduğu gibi Twitter’ ın da sunmuş olduğu API (Application Programing Interfaces-Uygulama Programlama Arayüzleri) hizmeti vasıtasıyla java tabanlı program kullanılarak Nisan-Mayıs 2016 tarihleri arasında alınmıştır. Elde edilen 8672 kullanıcı yorumu olumlu, nötr ve olumsuz etiketlerle ayrıştırılmıştır. Elde edilen etiketler etiket bulutunda toplanmış ve sonuçlar Makine Öğrenmesi Yöntemi ve SMO sınıflandırmasında standart ve normalize Kernel Polinomları ile analiz edilmiştir

References

  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R. Sentiment analysis of twitter data. In Proceedings of the ACL 2011 Workshop on Languages in Social Media (2011), pp. 30–38.
  • Aha, D. ve Kibler, D. (1991), “Instance-based learning algorithms”, Machine Learning, vol. 6, Issue no. 1, January 1991
  • Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning,20(3), 273-297.
  • Çetin, M., Amasyalı, M. F. (2013). Eğiticili ve Geleneksel Terim Ağırlıklandırma Yöntemleriyle Duygu Analizi. In Proceedings of Signal
  • Processing and Communications Applications Conference (SIU). Coban, O., Ozyer, B., Ozyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In Signal Processing and Communications
  • Applications Conference (SIU), 2015 23th (pp. 2388-2391). IEEE.
  • Çölkesen, İ. (2010). Destek Vektör Makineleri ile Uydu Görüntülerinin Sınıflandırılmasında Kernel Fonksiyonlarının Etkilerinin İncelenmesi. Harita Dergisi, 144, 73-82.
  • Das, S., Chen, M. (2001, July). Yahoo for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia
  • Pacific finance association annual conference (APFA) (Vol. 35, p. 43). Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
  • Eliaçık, A. B., Erdoğan, N. 2015). Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi.
  • Hutton, G., & Fosdick, M. (2011). The globalization of social media. Journal of Advertising Research, 51, 564–570.
  • Leo Breiman, Machine Learning, 45, 5–32, 2001: Random Forests
  • Meral, M., Diri, B. (2014). Twitter Üzerinde Duygu Analizi. IEEE 22nd Signal Processing and Communications Applications Conference (SIU). 693.
  • Nizam, H., Akın, S. S. (2014). Sosyal Medyada Makine Öğrenmesi ile Duygu Analizinde Dengeli ve Dengesiz Veri Setlerinin Performanslarının
  • Karşılaştırılması. XIX. Türkiye'de İnternet Konferansı. Osuna, E., Freund, R., & Girosi, F. (1997). Support vector machines: Training and applications.
  • Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1-2), 1-135.
  • Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.
  • Qualman, E. (2010). Socialnomics: How social media transforms the way we live and do business. John Wiley & Sons.
  • Tong, R. M. (2001). An operational system for detecting and tracking opinions in on-line discussion. In Working Notes of the ACM SIGIR
  • Workshop on Operational Text Classification (Vol. 1, p. 6). Wiebe, J. M. (1994). Tracking point of view in narrative. Computational Linguistics, 20(2), 233-287.

DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING

Year 2016, Volume: 2 Issue: 1, 684 - 691, 01.06.2016
https://doi.org/10.17261/Pressacademia.2016118690

Abstract

The identification of actual and potential customers opinions and sentiments before and after purchase shapes the services offered by airlines in the airline sector as well as in every sector. In this paper, a sentiment analysis is made by compiling Twitter users’ comments related to air transport. Comments of users collected from API (Application Programming Interfaces) service provided by Twitter as with many social media applications and were taken from a Java based program on a regular basis between April-May 2016. Obtained 8672 user comments were decomposed as positive, notr and negative tags. Tags are collected in a tag cloud and results are analysed with Machine Learning Method and standardized and normalized Kernel Polinoms in SMO algorithm

References

  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R. Sentiment analysis of twitter data. In Proceedings of the ACL 2011 Workshop on Languages in Social Media (2011), pp. 30–38.
  • Aha, D. ve Kibler, D. (1991), “Instance-based learning algorithms”, Machine Learning, vol. 6, Issue no. 1, January 1991
  • Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning,20(3), 273-297.
  • Çetin, M., Amasyalı, M. F. (2013). Eğiticili ve Geleneksel Terim Ağırlıklandırma Yöntemleriyle Duygu Analizi. In Proceedings of Signal
  • Processing and Communications Applications Conference (SIU). Coban, O., Ozyer, B., Ozyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In Signal Processing and Communications
  • Applications Conference (SIU), 2015 23th (pp. 2388-2391). IEEE.
  • Çölkesen, İ. (2010). Destek Vektör Makineleri ile Uydu Görüntülerinin Sınıflandırılmasında Kernel Fonksiyonlarının Etkilerinin İncelenmesi. Harita Dergisi, 144, 73-82.
  • Das, S., Chen, M. (2001, July). Yahoo for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia
  • Pacific finance association annual conference (APFA) (Vol. 35, p. 43). Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
  • Eliaçık, A. B., Erdoğan, N. 2015). Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi.
  • Hutton, G., & Fosdick, M. (2011). The globalization of social media. Journal of Advertising Research, 51, 564–570.
  • Leo Breiman, Machine Learning, 45, 5–32, 2001: Random Forests
  • Meral, M., Diri, B. (2014). Twitter Üzerinde Duygu Analizi. IEEE 22nd Signal Processing and Communications Applications Conference (SIU). 693.
  • Nizam, H., Akın, S. S. (2014). Sosyal Medyada Makine Öğrenmesi ile Duygu Analizinde Dengeli ve Dengesiz Veri Setlerinin Performanslarının
  • Karşılaştırılması. XIX. Türkiye'de İnternet Konferansı. Osuna, E., Freund, R., & Girosi, F. (1997). Support vector machines: Training and applications.
  • Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1-2), 1-135.
  • Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.
  • Qualman, E. (2010). Socialnomics: How social media transforms the way we live and do business. John Wiley & Sons.
  • Tong, R. M. (2001). An operational system for detecting and tracking opinions in on-line discussion. In Working Notes of the ACM SIGIR
  • Workshop on Operational Text Classification (Vol. 1, p. 6). Wiebe, J. M. (1994). Tracking point of view in narrative. Computational Linguistics, 20(2), 233-287.
There are 20 citations in total.

Details

Other ID JA95NE35JT
Journal Section Articles
Authors

Bahri Baran Kocak

İnci Polat This is me

Cem Burak Kocak This is me

Publication Date June 1, 2016
Published in Issue Year 2016 Volume: 2 Issue: 1

Cite

APA Kocak, B. B., Polat, İ., & Kocak, C. B. (2016). DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING. PressAcademia Procedia, 2(1), 684-691. https://doi.org/10.17261/Pressacademia.2016118690
AMA Kocak BB, Polat İ, Kocak CB. DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING. PAP. June 2016;2(1):684-691. doi:10.17261/Pressacademia.2016118690
Chicago Kocak, Bahri Baran, İnci Polat, and Cem Burak Kocak. “DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING”. PressAcademia Procedia 2, no. 1 (June 2016): 684-91. https://doi.org/10.17261/Pressacademia.2016118690.
EndNote Kocak BB, Polat İ, Kocak CB (June 1, 2016) DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING. PressAcademia Procedia 2 1 684–691.
IEEE B. B. Kocak, İ. Polat, and C. B. Kocak, “DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING”, PAP, vol. 2, no. 1, pp. 684–691, 2016, doi: 10.17261/Pressacademia.2016118690.
ISNAD Kocak, Bahri Baran et al. “DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING”. PressAcademia Procedia 2/1 (June 2016), 684-691. https://doi.org/10.17261/Pressacademia.2016118690.
JAMA Kocak BB, Polat İ, Kocak CB. DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING. PAP. 2016;2:684–691.
MLA Kocak, Bahri Baran et al. “DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING”. PressAcademia Procedia, vol. 2, no. 1, 2016, pp. 684-91, doi:10.17261/Pressacademia.2016118690.
Vancouver Kocak BB, Polat İ, Kocak CB. DETERMINATION OF TWITTER USERS SENTIMENT POLARITY TOWARD AIRLINE MARKET IN TURKEY: A CASE OF OPINION MINING. PAP. 2016;2(1):684-91.

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