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Metaverse İle İlgili Türkçe Dilindeki Çeşitli Sosyal Medya Platformu Verileri İle Duygu Analizi

Year 2022, Volume: 4 Issue: 2, 1 - 16, 31.12.2022

Abstract

Kısa zamanda popüler bir kavram olmayı başaran metaverse kavramı hakkında günümüzde olumlu veya olumsuz birçok yorum ve düşünce bulunmaktadır. Böylesine yeni bir girişim birçok insanı heyecanladırsa da birçok insanı da çekimser kılmaktadır. Bu çalışmada da metaverse hakkında Youtube ve Twitter olmak üzere iki sosyal medya platformundan elde edilen veriler ile bir metin madenciliği çalışması yapılmıştır. Elde edilen veriler ilk önce gönüllü insanlar vasıtasıyla olumlu ve olumsuz olarak etiketlenmiş sonrasında ise belirli veri seçme kriterlerine göre ayrıştırılıp, birleştirilmiştir. Platform bazında incelendiğinde Youtube yorumlarında daha fazla sayıda olumlu görüş belirten içerik olduğu görülmüştür. Twitter’da ise olumsuz görüşleri içeren içerik sayısı daha fazladır. Kullanılan veri setindeki toplam kelime sayılarına bakıldığında Twitter’dan elde edilen veri setindeki kelime sayısının Youtube’dan elde edilen veri setindeki kelime sayısından daha az olduğu görülmüştür. Analiz bölümünde makine öğrenmesi algoritmaları olarak Naive Bayes, Lojistik Regresyon, Destek Vektör Makineleri ve Rassal Orman sınıflandırıcıları kullanılmıştır. 1350 adet Youtube, 1350 adet de Twitter olmak üzere toplamda 2700 adet veriyle yapılan analiz sonucunda uygulanan bütün sınıflandırma algoritmaları yüzde seksen üzeri bir başarı göstermiş ve %88 ile Naive Bayes en başarılı algoritma olmuştur.

References

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  • [43] F. Zhang, H. Fleyeh, W. X. ve M. Lu, «Construction site accident analysis using text mining and natural language processing techniques.,» Automation in Construction, 2019.
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  • [45] K. Sailunaz ve R. Alhajj, «Emotion and sentiment analysis from Twitter text,» Journal of Computational Science, 2019.
  • [46] B. Jeong, J. Yoon ve J. Lee, «Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis.,» International Journal of Information Management, 2019.
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  • [54] S. Dirik, «Turkish_Sentiment_Analysis_ With_Multinomial_Naive_Bayes,» 23 Ağustos 2019. [Çevrimiçi]. Available:https://github.com/slmttndrk/Turkish_Sentiment_Analysis_With_Multinomial_Naive_Bayes.
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Year 2022, Volume: 4 Issue: 2, 1 - 16, 31.12.2022

Abstract

References

  • [1] N. Stephenson, Snow Crash, New York: A Novel; Random House Publishing Group, 2003.
  • [2] M. Köse, «Metaverse Nedir ve Neden Çok Önemlidir? Yaşamlarımızı Dijital Bir Evrene Taşıyabilir miyiz?,» 20 01 2022. [Çevrimiçi]. Available: https://evrimagaci.org/meta verse-nedir-veneden-cok-onemlidir-yasamlarimizi-dijital-bir-evrenetasiyabilir-miyiz-11135.
  • [3] H. Duan, J. Li, S. Fan, Z. Lin, X. Wu und W. Cai, „Metaverse for social good: A university campus prototype,“ in In Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, 2021.
  • [4] O. Kuş, «Metaverse: Perceptions Regarding Opportunities and Concerns in the 'Digital Big Bang',» Intermedia International e-Journal, pp. 245-266, 2021.
  • [5] E. Özkahveci, F. Civek ve G. Ulusoy, «The Place Of Metaverse (Fictional Universe) In The Period Of Industry 5.0,» JOURNAL OF SOCIAL HUMANITIES AND ADMINISTRATIVE SCIENCES, 2022. [6] A. Aydın, «SANAL GERÇEKLİK VE ARTIRILMIŞ GERÇEKLİK,» EĞİTİMDE DİJİTALLEŞME VE YENİ YAKLAŞIMLAR, ISTANBUL, Efe Akademi Yayınevi, 2021, pp. 7-25.
  • [7] Y. Mengli, Z. Ronggang, W. Huiwen ve Z. Weihua, «Yu M, Zhou R, Wang H, Zhao W. An evaluation for VR glasses system user experience: The influence factors of interactive operation and motion sickness.,» Appl Ergon, 2019.
  • [8] Neapolitan, R. E. ve Jiang, X. (2018). Artificial intelligence: With an introduction to machine learning (2. bs.). Boca Raton, FL, USA: Chapman & Hall/CRC.
  • [9] C. Arf, «Makine Düşünebilir Mi ve Nasıl Düşünebilir?,» 1958-1959 ÖĞRETİM YILI HALK KONFERANSLARI, Erzurum, 1959. [10] M. Hearst, «Untangling Text Data Mining The 37th Annual Meeting of the Association for Computational Linguistics, Maryland, 1999.
  • [11] D. Delen ve M. & Crossland, «Seeding the survey and analysis of research literature with text,» Expert Systems with Applications, 2008.
  • [12] H. Yılmaz ve S. Yumuşak, «Open Source Natural Language Processing Libraries,» Istanbul Sabahattin Zaim University Journal of the Institute of Science and Technology, 2021.
  • [13] M. K. Çelenli, «Sentiment Analizi (Duygu Analizi) Nedir?,» TAVTechNews, 2018.
  • [14] A. Bakirov, K. N. Çoğalmış ve A. Bulut, «Scalable sentiment analytics,» Turkish Journal of Electrical Engineering and Computer Science, 2016.
  • [15] A. Yanık ve S. Özçiçek, «Akıllı Telefon Bağımlılığında Sosyal Medya ve Oyunların Etkilerini Anlamak,» Uluslararası Halkbilimi Araştırmaları Dergisi, pp. 177-192, 2021.
  • [16] Statista, «Soziale Medien - Statistiken und Fakten,» Statista Research Department, 2022.
  • [17] A. Katal, M. Wazid ve R. H. Goudar, «Big data: Issues, challenges, tools and Good practices,» 2013 Sixth International Conference on Contemporary Computing (IC3), Noida, 2013.
  • [18] Statista, «What is the most popular social media platform worldwide?,» Statista Research Department, 2022.
  • [19] L. Zhang, S. Wang ve B. Liu, «Deep learning for sentiment analysis: A survey.,» Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018.
  • [20] Y. Ma, H. Peng ve E. & Cambria, «Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM,» 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018.
  • [21] D. Yang, J. Kleissl, C. A. Gueymard, H. T. C. Pedro ve C. F. M. Coimbra, « History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining.,» Solar Energy, no. 68, pp. 60-101, 2018.
  • [22] E. Cambria, S. Poria, D. Hazarika ve K. & Kwok, «SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings.,» 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018.
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  • [25] A. R. Alaei, S. Becken ve B. Stantic, «Sentiment Analysis in Tourism: Capitalizing on Big Data,» Journal of Travel Research, 2019.
  • [26] D. M. E. M. Hussein, «A survey on sentiment analysis challenges,» Journal of King Saud University - Engineering Sciences, 2018.
  • [27] H. H. Do, P. W. C. Prasad, A. Maag ve A. Alsadoon, «Deep learning for aspect-based sentiment analysis: A comparative review.,» Expert Systems with Applications, 2019.
  • [28] M. E. Basiri, S. Nemati, M. Abdar, E. Cambria ve U. R. Acharya, «ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis.,» Future Generation Computer Systems, 2021.
  • [29] H. Xu, B. Liu, L. Shu ve P. S. Yu, «BERT post-training for review reading comprehension and aspect-based sentiment analysis,» NAACL HLT 2019, Minneapolis, 2019.
  • [30] J. R. Ragini, P. M. R. Anand ve V. Bhaskar, «Big data analytics for disaster response and recovery through sentiment analysis,» International Journal of Information Management, 2018.
  • [31] C. Sun, L. Huang ve X. & Qiu, «Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence,» NAACL HLT 2019, Minneapolis, 2019.
  • [32] M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh ve B. Gupta, «Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of arabic hotels’ reviews.,» Journal of Computational Science, pp. 386-393, 2018.
  • [33] G. Xu, Y. Meng, X. Qiu, Z. Yu ve X. Wu, «Sentiment analysis of comment texts based on BiLSTM.,» IEEE Access, pp. 51522 - 515322019, 2019.
  • [34] Y. Ma, H. Peng, T. Khan, E. Cambria ve A. Hussain, «Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis,» Cognitive Computation, 2018.
  • [35] N. Majumder, D. Hazarika, A. Gelbukh, E. Cambria ve S. Poria, «Multimodal sentiment analysis using hierarchical fusion with context modeling.,» Knowledge-Based Systems, 2018.
  • [36] S. M. Rezaeinia, R. Rahmani, A. Ghodsi ve H. Veisi, «Sentiment analysis based on improved pre-trained word embeddings,» Expert Systems with Applications, 2019.
  • [37] S. Sohangir, D. Wang, A. Pomeranets ve T. M. Khoshgoftaar, «Big data: Deep learning for financial sentiment analysis.,» Journal of Big Data, 2018. [38] I. Chaturvedi, E. Cambria, R. E. Welsch ve F. Herrera, «Distinguishing between facts and opinions for sentiment analysis: Survey and challenges.,» Information Fusion, 2018.
  • [39] A. Yadav ve D. K. Vishwakarma, «Sentiment analysis using deep learning architectures: A review.,» Artificial Intelligence Review, 2020.
  • [40] A. Amado, P. Cortez, P. Rita ve S. Moro, «Research trends on big data in marketing: A text mining and topic modeling based literature analysis,» European Research on Management and Business Economics, 2018.
  • [41] S. Zhang, Z. Wei, Y. Wang ve T. Liao, «Sentiment analysis of chinese micro-blog text based on extended sentiment dictionary.,» Future Generation Computer Systems, 2018.
  • [42] N. Öztürk ve S. Ayvaz, «Sentiment analysis on twitter: A text mining approach to the syrian refugee crisis.,» Telematics and Informatics, 2018.
  • [43] F. Zhang, H. Fleyeh, W. X. ve M. Lu, «Construction site accident analysis using text mining and natural language processing techniques.,» Automation in Construction, 2019.
  • [44] Y. Wang, A. Sun, J. Han, Y. Liu ve X. Zhu, «Sentiment analysis by capsules.,» Lyon, Lyon, 2018
  • [45] K. Sailunaz ve R. Alhajj, «Emotion and sentiment analysis from Twitter text,» Journal of Computational Science, 2019.
  • [46] B. Jeong, J. Yoon ve J. Lee, «Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis.,» International Journal of Information Management, 2019.
  • [47] C. -.. Shen, M. Chen ve C. -.. Wang, «Analyzing the trend of O2O commerce by bilingual text mining on social media.,» Computers in Human Behavior, 2019.
  • [48] F. Hemmatian ve M. K. Sohrabi, «A survey on classification techniques for opinion mining and sentiment analysis.,» Artificial Intelligence Review, 2019.
  • [49] Ö. Ağralı ve Ö. Aydın, «Tweet Classification and Sentiment Analysis on Metaverse Related Messages,» Journal of Metaverse, 2021.
  • [50] H. Lee ve Y. Hwang, «Technology-Enhanced Education through VR-Making and Metaverse-Linking to Foster Teacher Readiness and Sustainable Learning,» Sustainability, 2022.
  • [51] S. Tunca, B. Sezen ve Y. S. Balcıoğlu, «TWITTER ANALYSIS FOR METAVERSE LITERACY,» INTERNATIONAL NEW YORK ACADEMIC RESEARCH CONGRESS, 2022.
  • [52] E. Dursunüstün, «The Importance of Testing in the Software Lifecycle and Automation of a Test,» Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2020.
  • [53] M. Meral ve B. Diri, «Sentiment analysis on Twitter,» Published in: 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, 2014.
  • [54] S. Dirik, «Turkish_Sentiment_Analysis_ With_Multinomial_Naive_Bayes,» 23 Ağustos 2019. [Çevrimiçi]. Available:https://github.com/slmttndrk/Turkish_Sentiment_Analysis_With_Multinomial_Naive_Bayes.
  • [55] R. Patil ve S. Kolhe, «Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets,» Social Network Analysis and Mining, 2022.
  • [56] A. C. Tantuğ , "Metin Sınıflandırma", Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, c. 5, sayı. 2, Haz. 2016
There are 53 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Cilt 4 - Sayı 2 - 31 December 2022 [en]
Authors

Ulaş Naki Turan 0000-0001-7406-2798

İlkim Ecem Emre 0000-0001-9507-8967

Selçuk Kıran 0000-0001-6088-2701

Publication Date December 31, 2022
Published in Issue Year 2022 Volume: 4 Issue: 2

Cite

APA Turan, U. N., Emre, İ. E., & Kıran, S. (2022). Metaverse İle İlgili Türkçe Dilindeki Çeşitli Sosyal Medya Platformu Verileri İle Duygu Analizi. Journal of Information Systems and Management Research, 4(2), 1-16.