Research Article
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Yeni Medya Çalışmalarında Metin Madenciliğinin Kullanımı: Sistematik Bir İnceleme

Year 2023, Issue: 15, 35 - 47, 29.12.2023

Derya Şahin

https://doi.org/10.55609/yenimedya.1340590

Abstract

Metin madenciliği, bir metni metin kaynağı olarak kabul eden ve metin üzerinden yapısallaştırılmış veri elde etmeyi amaçlayan bir yöntemdir. Bu yöntem gerçek zamanlı bulgular elde etmek ve geniş veri setlerinin değerlendirilmesi neticesinde etkin sonuçlar elde edilmesi kapsamında güçlü bir potansiyel sunmaktadır. Özellikle yeni medya gibi zengin kaynaklardan elde edilen verileri anlamak ve kavramak için bu denli etkin ve güçlü yöntemleri kullanmak, insanları anlamak ve bütünü algılamamız adına değerlidir. Bu bağlamda çalışmanın amacı dünyada yeni medya ve metin madenciliği konulu çalışmaların son on yılda nasıl bir gelişim gösterdiğini ve temel eğilimlerini ortaya koymaktır. Araştırmanın amacı ve belirlenen özel kriterler doğrultusunda 2013-2023 yılları arasında yayınlanan ve Google Scholar’da taranan toplam 82 makale sistematik inceleme metodu kullanılarak analiz edilmiştir.

Keywords

metin madenciliği yeni medya yeni medyada metin madenciliği metin madenciliği uygulamaları metin madenciliğinin geleceği

References

  • Al-Daihani, S.M. & Abrahams, A. (2018). Analysis of academic libraries’ Facebook posts: Text and data analytics. J. Acad. Librariansh. 44, 216–225, doi:10.1016/j.acalib.2018.02.004
  • Al-Mahmoud, H. & Al- Razgan, M. (2015). Arabic Text Mining: A Systematic Review of the Published Literature 2002-2014, IEEE
  • Atan, S. (2020). Metin madenciliği: İmkanlar, yöntemler ve kısıtlar. Mehmet Akif Ersoy University Journal of Social Sciences Institute, 31, 220-239, https://orcid.org/0000-0003-3170-0969
  • Chen, W.K., Riantama, D., & Chen, L.S. (2020). Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry, sustainability, 13(1), 1-17, https://doi.org/10.3390/su13010268
  • Çalış, K., Gazdağı, O. & Yıldız, O. (2913). Reklam içerikli epostaların metin madenciliği yöntemleri ile otomatik tespiti. Bilişim Teknolojileri Dergisi, 6(1), 1-7.
  • Çelik, S. (2020). Metin madenciliği ile Shakespeare külliyatının incelenmesi. MANAS Sosyal Araştırmalar Dergisi, 9(3), 1344-1357.
  • Daihani, S.M., & Abrahams, A. (2016). A Text Mining Analysis of Academic Libraries' Tweets, The Journal of Academic Librarianship, 42(2), 135-143, https://doi.org/10.1016/j.acalib.2015.12.014
  • Feldman, R. & Dagan, I. (1995). Kdt - Knowledge discovery in texts. In Proc. of The First Int. Conf. on Knowledge Discovery (KDD), 112–117.
  • Göker, H. & Tekedere, H. (2017). FATİH projesine yönelik görüşlerin Metin madenciliği yöntemleri ile otomatik değerlendirilmesi. Bilişim Teknolojileri Dergisi, 10(3), 291-299, DOI: 10.17671/gazibtd.331041
  • Gupta, V. & Lehal, G.S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  • Gupta, B., Sharma, S. & Chennamaneni, A. (2016). Twitter sentiment analysis: An examination of cybersecurity attitudes and behavior. In Proceedings of the 2016 Pre-ICIS SIGDSA/IFIP WG8.3 Symposium: Innovations in Data Analytics, Dublin, Ireland, 11 December, 17.
  • Hanley, T. & Cutts, L. (2013). What is a systematic review. Counselling Psychology Review, 28(4), 3-6.
  • Hashimi, H., Hafez, A. & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior.
  • He, W., Zha, S. & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. Int. J. Inf. Manag, 33, 464–472, doi:10.1016/j.ijinfomgt.2013.01.001
  • Hotho, A., Nurnberger, A. & Paaß, G. (2005). A brief survey of text mining. LDV Forum - GLDV Journal for Computational Linguistics and Language Technology. 20(1). 19-62.
  • Karami, A., Lundy, M., Webb, F. & Dwivedi, Y.K. (2020). Twitter and Research: A Systematic Literature Review Through Text Mining, in IEEE Access, vol. 8, pp. 67698-67717, 2020, doi: 10.1109/ACCESS.2020.2983656.
  • Kılınç, D., Borandağ, E., Yücalar, F., Tunalı, V., Şimşek, M. & Özçift, A. (2016). KNN algoritması ve R dili ile metin madenciliği kullanılarak bilimsel makale tasnifi. Marmara Fen Bilimleri Dergisi, 28(3), 89-94.
  • Kim, J. & Hastak, M. (2018). Social network analysis: Characteristics of online social networks after a disaster. Int. J. Inf. Manag, 38, 86–96, doi:10.1016/j.ijinfomgt.2017.08.003
  • Koyuncugil, A.S. (2006). Bulanık veri madenciliği ve sermaye piyasalarına uygulanması. Ankara Üniversitesi Fen Bilimleri Enstitüsü, Yayınlanmış doktora tezi.
  • Kumar, S., Kar, A.K. & Ilavarasan, P.V. (2021). Applications of text mining in services management: A systematic literature review, Information Management Data Insights, 1(1), 1-11, https://doi.org/10.1016/j.jjimei.2021.100008
  • Loureda, M.C., Baquerio, A.H., & Tamés Muñoz, E. (2023). A text mining analysis of human fourishing onTwitter, Scientifc Reports, 13.
  • Needleman, I. G. (2002). A guide to systematic reviews. Journal of Clinical Periodontology, 29(s3), 6-9. https://doi.org/https://doi.org/10.1034/j.1600-051X.29.s3.15.x
  • Nisar, T.M. & Yeung, M. (2018). Twitter as a tool for forecasting stock market movements: A short-window event study. J. Financ. Data Sci, 4, 101–119, doi:10.1016/j.jfds.2017.11.002
  • Okmeydan, S. B. (2017). Yeni İletişim Teknolojilerini Sorgulamak: Etik, Güvenlik ve Mahremiyetin Kesiştiği Nokta. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 5 (1), 347-371.
  • Onan, A. (2021). COVID-19 ile ilgili sosyal medya gönderilerinin metin madenciliği yöntemlerine dayalı olarak zaman-mekansal analizi. Avrupa Bilim ve Teknoloji Dergisi, 26, 138-143.
  • Öztürk, N., & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis, Telematics and Informatics, 35, 136-147.
  • Pavlidou, I. & Tsui, E. (2020). Crowdsourcing: a systematic review of the literature using text mining, Industrial Management & Data Systems, 120(11), 2041-2065, DOI 10.1108/IMDS-08-2020-0474
  • Philer, K. & Zhong, Y. (2016). Twitter sentiment analysis: Capturing sentiment from integrated resort tweets. Int. J. Hosp. Manag, 55, 16–24, doi:10.1016/j.ijhm.2016.02.001
  • Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: A systematic review of the evidence. International journal of Management Reviews, 5(3-4), 137- 168.
  • Pilavcılar, İ.F. (2007). Metin madenciliği ile metin sınıflandırma. Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü. Yayımlanmış Published Yüksek lisans tezi.
  • Postman, N. (2016). Teknopoli Kültürün Teknolojiye Teslim Oluşu, Sentez Yayıncılık İstanbul.
  • Rea, B., Okazaki, N., Procter, R. & Thomas, J. (2009). Supporting Systematic ReviewsUsing Text Mining, Social Science Computer Review, 27(4), 509-523.
  • Salloum, S.A., Mhamdi, C., Al-Emran, M. & Shaalan, K. (2017). Analysis and classification of Arabic newspapers’ Facebook pages using text ining Techniques. Int. J. Inf. Technol. Lang. Stud, 1, 8–17.
  • Shen, C., Chen, M., & Wang, C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media, Computers in Human Behavior, 101, 474-483, https://doi.org/10.1016/j.chb.2018.09.031
  • Stieglitz, S., & Xuan, L.D. (2013). Social media and political communication: a social media analytics framework, Soc. Netw. Anal. Min., 3, 1277–1291.
  • Şeker, S.E. (2015). Metin madenciliği (text mining). YBS Ansiklopedi, 2(3), 30-32.
  • Tadesse, M.M., Lin, H., Xu, B., & Yang, L. (2019). Detection of Depression-Related Posts in Reddit Social Media Forum, IEEEAccess, 7, 44883 – 44893, 10.1109/ACCESS.2019.2909180
  • Temizhan, E. & Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Türkiye Klinikleri Biyoistatistik Dergisi, 13(2), 185-200, DOI: 10.5336/biostatic.2020-79992
  • Thelen, P. D. (2021). The status of public relations research addressing Latin America: A content analysis of published articles from 1980 to 2020. Public Relations Review, 47(4), 102079. https://doi.org/https://doi.org/10.1016/j.pubrev.2021.102079
  • Turenne, N. (2022). Net activism and whistleblowing on YouTube: a text mining analysis, Multimedia Tools and Applications, 82, 9201-9221, https://doi.org/10.1007/s11042-022-13777-0
  • Xiang, Z., Schwartz, Z., Gerdes, J.H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?, International Journal of Hospitality Management, 44, 120-130, https://doi.org/10.1016/j.ijhm.2014.10.013
  • Vijayarani, S., Ilamathi, J. & Nithya (2015). Preprocessing techniques for text mining - an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • Yost, E., Zhang, T., & Qi, R. (2021). The power of engagement: Understanding active social media engagement and the impact on sales in the hospitality industry, Journal of Hospitality and Tourism Management, 46, 83-95, https://doi.org/10.1016/j.jhtm.2020.10.008

The Use of Text Mining in New Media Studies: A Systematic Review

Year 2023, Issue: 15, 35 - 47, 29.12.2023

Derya Şahin

https://doi.org/10.55609/yenimedya.1340590

Abstract

Text mining is a method that relies on the text as the source and aims to get the data through the text. This method has a strong potential in obtaining real-time findings and effective results in terms of the evaluation of large data sets. It is valuable to understand the people and the whole picture by using such powerful and effective methods obtained from rich sources like new media. In this context, the aim of this study is to define the basic tendencies and the changes that happened in the studies on new media and text mining in the last decade. This study analyses 82 articles published on Google Scholar between 2013 and 2023. The articles were analysed using a systematic analysis method which contains specific criteria in accordance with the aim of this research.

Keywords

text mining new media text mining in new media text mining applications future of text mining

References

  • Al-Daihani, S.M. & Abrahams, A. (2018). Analysis of academic libraries’ Facebook posts: Text and data analytics. J. Acad. Librariansh. 44, 216–225, doi:10.1016/j.acalib.2018.02.004
  • Al-Mahmoud, H. & Al- Razgan, M. (2015). Arabic Text Mining: A Systematic Review of the Published Literature 2002-2014, IEEE
  • Atan, S. (2020). Metin madenciliği: İmkanlar, yöntemler ve kısıtlar. Mehmet Akif Ersoy University Journal of Social Sciences Institute, 31, 220-239, https://orcid.org/0000-0003-3170-0969
  • Chen, W.K., Riantama, D., & Chen, L.S. (2020). Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry, sustainability, 13(1), 1-17, https://doi.org/10.3390/su13010268
  • Çalış, K., Gazdağı, O. & Yıldız, O. (2913). Reklam içerikli epostaların metin madenciliği yöntemleri ile otomatik tespiti. Bilişim Teknolojileri Dergisi, 6(1), 1-7.
  • Çelik, S. (2020). Metin madenciliği ile Shakespeare külliyatının incelenmesi. MANAS Sosyal Araştırmalar Dergisi, 9(3), 1344-1357.
  • Daihani, S.M., & Abrahams, A. (2016). A Text Mining Analysis of Academic Libraries' Tweets, The Journal of Academic Librarianship, 42(2), 135-143, https://doi.org/10.1016/j.acalib.2015.12.014
  • Feldman, R. & Dagan, I. (1995). Kdt - Knowledge discovery in texts. In Proc. of The First Int. Conf. on Knowledge Discovery (KDD), 112–117.
  • Göker, H. & Tekedere, H. (2017). FATİH projesine yönelik görüşlerin Metin madenciliği yöntemleri ile otomatik değerlendirilmesi. Bilişim Teknolojileri Dergisi, 10(3), 291-299, DOI: 10.17671/gazibtd.331041
  • Gupta, V. & Lehal, G.S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  • Gupta, B., Sharma, S. & Chennamaneni, A. (2016). Twitter sentiment analysis: An examination of cybersecurity attitudes and behavior. In Proceedings of the 2016 Pre-ICIS SIGDSA/IFIP WG8.3 Symposium: Innovations in Data Analytics, Dublin, Ireland, 11 December, 17.
  • Hanley, T. & Cutts, L. (2013). What is a systematic review. Counselling Psychology Review, 28(4), 3-6.
  • Hashimi, H., Hafez, A. & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior.
  • He, W., Zha, S. & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. Int. J. Inf. Manag, 33, 464–472, doi:10.1016/j.ijinfomgt.2013.01.001
  • Hotho, A., Nurnberger, A. & Paaß, G. (2005). A brief survey of text mining. LDV Forum - GLDV Journal for Computational Linguistics and Language Technology. 20(1). 19-62.
  • Karami, A., Lundy, M., Webb, F. & Dwivedi, Y.K. (2020). Twitter and Research: A Systematic Literature Review Through Text Mining, in IEEE Access, vol. 8, pp. 67698-67717, 2020, doi: 10.1109/ACCESS.2020.2983656.
  • Kılınç, D., Borandağ, E., Yücalar, F., Tunalı, V., Şimşek, M. & Özçift, A. (2016). KNN algoritması ve R dili ile metin madenciliği kullanılarak bilimsel makale tasnifi. Marmara Fen Bilimleri Dergisi, 28(3), 89-94.
  • Kim, J. & Hastak, M. (2018). Social network analysis: Characteristics of online social networks after a disaster. Int. J. Inf. Manag, 38, 86–96, doi:10.1016/j.ijinfomgt.2017.08.003
  • Koyuncugil, A.S. (2006). Bulanık veri madenciliği ve sermaye piyasalarına uygulanması. Ankara Üniversitesi Fen Bilimleri Enstitüsü, Yayınlanmış doktora tezi.
  • Kumar, S., Kar, A.K. & Ilavarasan, P.V. (2021). Applications of text mining in services management: A systematic literature review, Information Management Data Insights, 1(1), 1-11, https://doi.org/10.1016/j.jjimei.2021.100008
  • Loureda, M.C., Baquerio, A.H., & Tamés Muñoz, E. (2023). A text mining analysis of human fourishing onTwitter, Scientifc Reports, 13.
  • Needleman, I. G. (2002). A guide to systematic reviews. Journal of Clinical Periodontology, 29(s3), 6-9. https://doi.org/https://doi.org/10.1034/j.1600-051X.29.s3.15.x
  • Nisar, T.M. & Yeung, M. (2018). Twitter as a tool for forecasting stock market movements: A short-window event study. J. Financ. Data Sci, 4, 101–119, doi:10.1016/j.jfds.2017.11.002
  • Okmeydan, S. B. (2017). Yeni İletişim Teknolojilerini Sorgulamak: Etik, Güvenlik ve Mahremiyetin Kesiştiği Nokta. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 5 (1), 347-371.
  • Onan, A. (2021). COVID-19 ile ilgili sosyal medya gönderilerinin metin madenciliği yöntemlerine dayalı olarak zaman-mekansal analizi. Avrupa Bilim ve Teknoloji Dergisi, 26, 138-143.
  • Öztürk, N., & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis, Telematics and Informatics, 35, 136-147.
  • Pavlidou, I. & Tsui, E. (2020). Crowdsourcing: a systematic review of the literature using text mining, Industrial Management & Data Systems, 120(11), 2041-2065, DOI 10.1108/IMDS-08-2020-0474
  • Philer, K. & Zhong, Y. (2016). Twitter sentiment analysis: Capturing sentiment from integrated resort tweets. Int. J. Hosp. Manag, 55, 16–24, doi:10.1016/j.ijhm.2016.02.001
  • Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: A systematic review of the evidence. International journal of Management Reviews, 5(3-4), 137- 168.
  • Pilavcılar, İ.F. (2007). Metin madenciliği ile metin sınıflandırma. Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü. Yayımlanmış Published Yüksek lisans tezi.
  • Postman, N. (2016). Teknopoli Kültürün Teknolojiye Teslim Oluşu, Sentez Yayıncılık İstanbul.
  • Rea, B., Okazaki, N., Procter, R. & Thomas, J. (2009). Supporting Systematic ReviewsUsing Text Mining, Social Science Computer Review, 27(4), 509-523.
  • Salloum, S.A., Mhamdi, C., Al-Emran, M. & Shaalan, K. (2017). Analysis and classification of Arabic newspapers’ Facebook pages using text ining Techniques. Int. J. Inf. Technol. Lang. Stud, 1, 8–17.
  • Shen, C., Chen, M., & Wang, C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media, Computers in Human Behavior, 101, 474-483, https://doi.org/10.1016/j.chb.2018.09.031
  • Stieglitz, S., & Xuan, L.D. (2013). Social media and political communication: a social media analytics framework, Soc. Netw. Anal. Min., 3, 1277–1291.
  • Şeker, S.E. (2015). Metin madenciliği (text mining). YBS Ansiklopedi, 2(3), 30-32.
  • Tadesse, M.M., Lin, H., Xu, B., & Yang, L. (2019). Detection of Depression-Related Posts in Reddit Social Media Forum, IEEEAccess, 7, 44883 – 44893, 10.1109/ACCESS.2019.2909180
  • Temizhan, E. & Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Türkiye Klinikleri Biyoistatistik Dergisi, 13(2), 185-200, DOI: 10.5336/biostatic.2020-79992
  • Thelen, P. D. (2021). The status of public relations research addressing Latin America: A content analysis of published articles from 1980 to 2020. Public Relations Review, 47(4), 102079. https://doi.org/https://doi.org/10.1016/j.pubrev.2021.102079
  • Turenne, N. (2022). Net activism and whistleblowing on YouTube: a text mining analysis, Multimedia Tools and Applications, 82, 9201-9221, https://doi.org/10.1007/s11042-022-13777-0
  • Xiang, Z., Schwartz, Z., Gerdes, J.H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?, International Journal of Hospitality Management, 44, 120-130, https://doi.org/10.1016/j.ijhm.2014.10.013
  • Vijayarani, S., Ilamathi, J. & Nithya (2015). Preprocessing techniques for text mining - an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • Yost, E., Zhang, T., & Qi, R. (2021). The power of engagement: Understanding active social media engagement and the impact on sales in the hospitality industry, Journal of Hospitality and Tourism Management, 46, 83-95, https://doi.org/10.1016/j.jhtm.2020.10.008
There are 43 citations in total.

Details

Primary Language English
Subjects New Media
Journal Section Research Articles
Authors

Derya Şahin 0000-0002-5894-1554

Early Pub Date December 25, 2023
Publication Date December 29, 2023
Submission Date August 10, 2023
Published in Issue Year 2023 Issue: 15

Cite

APA Şahin, D. (2023). The Use of Text Mining in New Media Studies: A Systematic Review. Yeni Medya(15), 35-47. https://doi.org/10.55609/yenimedya.1340590
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