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Veri ve metin madenciliği ile hava yolu işletmelerinin Covid-19 öncesi ve sonrası sosyal medya yorum ve skorlarının değerlendirilmesi

Year 2022, Volume: 15 Issue: 4, 998 - 1022, 25.10.2022

İbrahim Budak Arzu Organ

https://doi.org/10.25287/ohuiibf.1149801
Cited By: 1

Abstract

Veri ve metin madenciliği, anlamlı ilişkileri ve eğilimleri ayırt etmek için kullanıcıların taleplerine göre yapılandırılmış, yarı yapılandırılmış ve yapılandırılmamış büyük bir veri miktarını analiz etme sürecidir. İşletmeler, veri ve metin madenciliği teknikleri kullanarak hem kendi işletmeleri içerisinde hem de rakipleri ile rekabette yaşadıkları sorunlarına etkili çözümler üretebilmektedirler. Böylece elde ettikleri bilgiyi rekabet avantajına çevirebilmektedirler. Bu araştırmada, veri ve metin madenciliği algoritmaları kullanılarak rekabete dayalı pazarda müşterilerin istek ve ihtiyaçlarına göre hava yolu firmalarının üstün ve zayıf yönlerinin değerlendirilmesi amaçlanmıştır. Bu araştırmada, sosyal medya sitelerinden olan TripAdvisor’daki çevrimiçi seyahat incelemeleri araştırma kapsamına alınmıştır. Star Alliance küresel hava yolu birliğine üye 26 hava yolu firması değerlendirilmiştir. Araştırmada kullanılan kriterler; her bir kullanıcının yorum ve skorları temel alınarak belirlenmiştir. Duygu Analizi ile müşteri yorumlarından polariteleri belirlendikten sonra Destek Vektör Makineleri, Naive Bayes, Derin Öğrenme Algoritmaları ile sınıflandırma ve tahminleme yapılarak elde edilen sonuçlar karşılaştırılmıştır. Bunun yanı sıra sonuçlar, Covid-19 pandemisi öncesi ve sonrası olarak da karşılaştırılmıştır. Yapılan karşılaştırmada Derin Öğrenmenin daha iyi sonuç verdiği saptanmıştır.

Keywords

Veri ve Metin Madenciliği Hava Yolu Destek Vektör Makineleri Naive Bayes Derin Öğrenme

References

  • Agarap, A. F. (2018). Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375.
  • Alexa, https://www.alexa.com/siteinfo/tripadvisor.com#section_traffic (02.05.2020).
  • An, Y., Sun, S., & Wang, S. (2017, May). Naive Bayes classifiers for music emotion classification based on lyrics. In 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (pp. 635-638). IEEE.
  • BholaneSavita, D., & Gore, D. (2016). Sentiment analysis on twitter data using support vector machine. International Journal of Computer Science Trends and Technology (IJCST)–Volume, 4, 365-370.
  • Cherian, V., & Bindu, M. S. (2017). Heart disease prediction using Naïve Bayes algorithm and Laplace smoothing technique. International Journal of Computer Science Trends and Technology (IJCST), 5(2), 68-73.
  • 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).
  • Day, M. Y., & Lin, Y. D. (2017, August). Deep learning for sentiment analysis on google play consumer review. In 2017 IEEE international conference on information reuse and integration (IRI) (pp. 382-388). IEEE.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press..
  • Haddaway, N. R. (2015). The use of web-scraping software in searching for grey literature. Grey Journal, 11(3), 186-190.
  • Harisinghaney, A., Dixit, A., Gupta, S., & Arora, A. (2014, February). Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) (pp. 153-155). IEEE.
  • Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., & Kaymak, U. (2013, March). Exploiting emoticons in sentiment analysis. In Proceedings of the 28th annual ACM symposium on applied computing (pp. 703-710).
  • Hussein, S. M., Ali, F. H. M., & Kasiran, Z. (2012, May). Evaluation effectiveness of hybrid IDs using snort with naive Bayes to detect attacks. In 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP) (pp. 256-260). IEEE..
  • Karamanlı, E. (2019). Makine Öğrenmesi Algoritmaları Kullanarak Metin Madenciliği ve Duygu Analizi ile Müşteri Deneyiminin Geliştirilmesi (Doctoral dissertation, Yüksek Lisans Tezi, İstanbul Üniversitesi, İstanbul, Türkiye).
  • Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971, 5-15.
  • Kuhamanee, T., Talmongkol, N., Chaisuriyakul, K., San-Um, W., Pongpisuttinun, N., & Pongyupinpanich, S. (2017, July). Sentiment analysis of foreign tourists to Bangkok using data mining through online social network. In 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) (pp. 1068-1073). IEEE.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
  • Liu, H., & Cocea, M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2(4), 357-386.
  • Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML (Vol. 30, No. 1, p. 3-8).
  • Makhabel, B., Mishra P., Danneman, N. & Heimann, R. (2017). R: Mining Spatial, Text, Web, and Social Media Data. Packt Publishing.
  • Melek, C. (2012). Metin madenciliği teknikleri ile şirketlerin vizyon ifadelerinin analizi Doctoral dissertation, DEÜ Sosyal Bilimleri Enstitüsü. Kütahya.
  • Narayanan, V., Arora, I., & Bhatia, A. (2013, October). Fast and accurate sentiment classification using an enhanced Naive Bayes model. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 194-201). Springer, Berlin, Heidelberg.
  • Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77).
  • Povoda, L., Burget, R., & Dutta, M. K. (2016, June). Sentiment analysis based on support vector machine and big data. In 2016 39th International Conference on Telecommunications and Signal Processing (TSP) (pp. 543-545). IEEE
  • Sharma, P., Singh, D., & Singh, A. (2015, February). Classification algorithms on a large continuous random dataset using rapid miner tool. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 704-709). IEEE.
  • Sivil Havacılık Genel Müdürlüğü, http://web.shgm.gov.tr/ (17.01.2021).
  • Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
  • Taha, A. M., Mustapha, A., & Chen, S. D. (2013). Naive Bayes-guided bat algorithm for feature selection. The Scientific World Journal, 1-10.
  • Terzic, J., Terzic, E., Nagarajah, R., & Alamgir, M. (2013). Ultrasonic fluid quantity measurement in dynamic vehicular applications. Springer International Pu.
  • Tong, S., & Chang, E. (2001, October). Support vector machine active learning for image retrieval. In Proceedings of the ninth ACM international conference on Multimedia (pp. 107-118).
  • Tripathi, P., Vishwakarma, S. K., & Lala, A. (2015, December). Sentiment analysis of english tweets using rapid miner. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 668-672). IEEE.
  • Tsao, H. Y., Chen, M. Y., Lin, H. C. K., & Ma, Y. C. (2019). The asymmetric effect of review valence on numerical rating: A viewpoint from a sentiment analysis of users of TripAdvisor. Online Information Review, 43(2), 283-300.
  • Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
  • Zhang, L., Zhou, W., & Jiao, L. (2004). Wavelet support vector machine. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 34-39.

Evaluation of social media comments and scores of airline companies before and after Covid-19 with data and text mining

Year 2022, Volume: 15 Issue: 4, 998 - 1022, 25.10.2022

İbrahim Budak Arzu Organ

https://doi.org/10.25287/ohuiibf.1149801
Cited By: 1

Abstract

Data and text mining is the process of analyzing a large amount of semi-structured, unstructured and structured data based on users' demands to distinguish meaningful relationships and trends. By using the data and text mining techniques, companies can produce effective solutions to the problems within their business and with their competitors. Thus, they can turn this information into competitive advantage. In this study, it is aimed to evaluate the strengths and weaknesses of airline companies in competitive market according to the demands and needs of customers through using the data and text mining algorithms. In this research, online user reviews on TripAdvisor which is one of the social media sites was included in this research. 26 airlines which are the members of Star Alliance Global Airline Association were evaluated. The criteria used in this research was determined based on the comments and scores of each user. After determining the polarities from customer comments with Sentiment Analysis, the results obtained by classification and estimation with Support Vector Machines, Naive Bayes, Deep Learning Algorithms were compared. In addition, the results were compared before and after the Covid-19 pandemic. In the comparison made, it was determined that Deep learning gives better results.

Keywords

Data andMining Airline Support Vector Machines Naive Bayes Deep Learning

References

  • Agarap, A. F. (2018). Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375.
  • Alexa, https://www.alexa.com/siteinfo/tripadvisor.com#section_traffic (02.05.2020).
  • An, Y., Sun, S., & Wang, S. (2017, May). Naive Bayes classifiers for music emotion classification based on lyrics. In 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (pp. 635-638). IEEE.
  • BholaneSavita, D., & Gore, D. (2016). Sentiment analysis on twitter data using support vector machine. International Journal of Computer Science Trends and Technology (IJCST)–Volume, 4, 365-370.
  • Cherian, V., & Bindu, M. S. (2017). Heart disease prediction using Naïve Bayes algorithm and Laplace smoothing technique. International Journal of Computer Science Trends and Technology (IJCST), 5(2), 68-73.
  • 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).
  • Day, M. Y., & Lin, Y. D. (2017, August). Deep learning for sentiment analysis on google play consumer review. In 2017 IEEE international conference on information reuse and integration (IRI) (pp. 382-388). IEEE.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press..
  • Haddaway, N. R. (2015). The use of web-scraping software in searching for grey literature. Grey Journal, 11(3), 186-190.
  • Harisinghaney, A., Dixit, A., Gupta, S., & Arora, A. (2014, February). Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) (pp. 153-155). IEEE.
  • Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., & Kaymak, U. (2013, March). Exploiting emoticons in sentiment analysis. In Proceedings of the 28th annual ACM symposium on applied computing (pp. 703-710).
  • Hussein, S. M., Ali, F. H. M., & Kasiran, Z. (2012, May). Evaluation effectiveness of hybrid IDs using snort with naive Bayes to detect attacks. In 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP) (pp. 256-260). IEEE..
  • Karamanlı, E. (2019). Makine Öğrenmesi Algoritmaları Kullanarak Metin Madenciliği ve Duygu Analizi ile Müşteri Deneyiminin Geliştirilmesi (Doctoral dissertation, Yüksek Lisans Tezi, İstanbul Üniversitesi, İstanbul, Türkiye).
  • Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971, 5-15.
  • Kuhamanee, T., Talmongkol, N., Chaisuriyakul, K., San-Um, W., Pongpisuttinun, N., & Pongyupinpanich, S. (2017, July). Sentiment analysis of foreign tourists to Bangkok using data mining through online social network. In 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) (pp. 1068-1073). IEEE.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
  • Liu, H., & Cocea, M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2(4), 357-386.
  • Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML (Vol. 30, No. 1, p. 3-8).
  • Makhabel, B., Mishra P., Danneman, N. & Heimann, R. (2017). R: Mining Spatial, Text, Web, and Social Media Data. Packt Publishing.
  • Melek, C. (2012). Metin madenciliği teknikleri ile şirketlerin vizyon ifadelerinin analizi Doctoral dissertation, DEÜ Sosyal Bilimleri Enstitüsü. Kütahya.
  • Narayanan, V., Arora, I., & Bhatia, A. (2013, October). Fast and accurate sentiment classification using an enhanced Naive Bayes model. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 194-201). Springer, Berlin, Heidelberg.
  • Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77).
  • Povoda, L., Burget, R., & Dutta, M. K. (2016, June). Sentiment analysis based on support vector machine and big data. In 2016 39th International Conference on Telecommunications and Signal Processing (TSP) (pp. 543-545). IEEE
  • Sharma, P., Singh, D., & Singh, A. (2015, February). Classification algorithms on a large continuous random dataset using rapid miner tool. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 704-709). IEEE.
  • Sivil Havacılık Genel Müdürlüğü, http://web.shgm.gov.tr/ (17.01.2021).
  • Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
  • Taha, A. M., Mustapha, A., & Chen, S. D. (2013). Naive Bayes-guided bat algorithm for feature selection. The Scientific World Journal, 1-10.
  • Terzic, J., Terzic, E., Nagarajah, R., & Alamgir, M. (2013). Ultrasonic fluid quantity measurement in dynamic vehicular applications. Springer International Pu.
  • Tong, S., & Chang, E. (2001, October). Support vector machine active learning for image retrieval. In Proceedings of the ninth ACM international conference on Multimedia (pp. 107-118).
  • Tripathi, P., Vishwakarma, S. K., & Lala, A. (2015, December). Sentiment analysis of english tweets using rapid miner. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 668-672). IEEE.
  • Tsao, H. Y., Chen, M. Y., Lin, H. C. K., & Ma, Y. C. (2019). The asymmetric effect of review valence on numerical rating: A viewpoint from a sentiment analysis of users of TripAdvisor. Online Information Review, 43(2), 283-300.
  • Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
  • Zhang, L., Zhou, W., & Jiao, L. (2004). Wavelet support vector machine. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 34-39.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Operation, Business Administration
Journal Section Articles
Authors

İbrahim Budak 0000-0001-7762-6114

Arzu Organ 0000-0002-2400-4343

Early Pub Date October 26, 2022
Publication Date October 25, 2022
Submission Date July 27, 2022
Acceptance Date September 16, 2022
Published in Issue Year 2022 Volume: 15 Issue: 4

Cite

APA Budak, İ., & Organ, A. (2022). Veri ve metin madenciliği ile hava yolu işletmelerinin Covid-19 öncesi ve sonrası sosyal medya yorum ve skorlarının değerlendirilmesi. Ömer Halisdemir Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 15(4), 998-1022. https://doi.org/10.25287/ohuiibf.1149801

Cited By

VERİ MADENCİLİĞİ İLE HAVA YOLU MÜŞTERİ DEĞERLENDİRMELERİNİN ANALİZİ: BİR KARAR AĞACI ALGORİTMASI ÖRNEĞİ
Gaziosmanpasa Universitesi Sosyal Bilimler Arastirmalari Dergisi
https://doi.org/10.48145/gopsbad.954125
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