SavedAdd your journal

Journals / Journal of Metaverse / 2021 / Volume: 1 Issue: 1

Tweet Classification and Sentiment Analysis on Metaverse Related Messages

Özgür Ağralı(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0000-0001-6974-011X
Ömer Aydın(MANİSA CELÂL BAYAR ÜNİVERSİTESİ)0000-0002-7137-4881
Pages
—
Publication Date
2021-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Obtained findings and results are shared comparatively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The data obtained from social media platforms is a popular study subject nowadays.
📊
Key Findings & Data:These studies give important information about the thoughts of the society towards an event, situation, or concept.
💡
Conclusion & Impact:Obtained findings and results are shared comparatively.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Sentiment analysis · Metaverse · tweet classification

Abstract

The data obtained from social media platforms is a popular study subject nowadays. These studies give important information about the thoughts of the society towards an event, situation, or concept. For this purpose, several studies have been carried out with different methods in the literature. These studies mainly try to obtain meaningful results by applying various methods according to the language of the social media content. One of these platforms where people freely express their feelings and ideas is Twitter. It is a popular and useful study to examine people's feelings and tendencies about a topic by doing tweet analysis. In this study, feelings about Metaverse are tried to be evaluated. We evaluate the tweets posted one week ago and later of the date Mark Zuckerberg announced that her company would change its name to Meta. Tweets sent in English with the "metaverse" hashtag on Twitter were used as the dataset. These tweets were analysed by the Sentiment Analysis method. Obtained findings and results are shared comparatively.

🔖

Cite This Article

Ağralı, Ö., Aydın, Ö. (2021). Tweet Classification and Sentiment Analysis on Metaverse Related Messages. Journal of Metaverse, Volume: 1 Issue: 1. https://ajindex.com/articles/1618996

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Pang, B., & Lee, L.J. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135. Doi: 10.1561/1500000011
  2. Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058
  3. Read, J. (2005, June). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL student research workshop (pp. 43-48).
  4. Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
  5. Çoban, Ö., & Tümüklü-Özyer, G. (2018). Twitter duygu analizinde terim ağırlıklandırma yönteminin etkisi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(2), 283-291. doi: 10.5505/pajes.2017.50480
  6. Sarlan, A., Nadam, C., & Basri, S. (2014, November). Twitter sentiment analysis. In Proceedings of the 6th International conference on Information Technology and Multimedia (pp. 212-216). IEEE.
  7. Karayiğit, H., Çiğdem, A. C. I., & Akdağlı, A. (2018). A Review of Turkish Sentiment Analysis and Opinion Mining. Balkan Journal of Electrical and Computer Engineering, 6(2), 94-98. DOI: 10.17694/bajece.419547
  8. Abalı, G., Karaarslan, E., Hürriyetoğlu, A., & Dalkılıç, F. (2018, April). Detecting citizen problems and their locations using twitter data. In 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG) (pp. 30-33). IEEE.
  9. Ayan, B., Kuyumcu, B., & Ciylan, B. (2019). Detection of Islamophobic Tweets on Twitter Using Sentiment Analysis. Gazi Üniversitesi Fen Bilimleri Dergisi, 7(2), 495-502. DOI:10.29109/gujsc.561806
  10. Fadel, I.A., & Öz; C. (2020), A Sentiment Analysis Model for Terrorist Attacks Reviews on Twitter. Sakarya University Journal of Science, 24(6), 1294-1302, DOI: 10.16984/saufenbilder.711612
  11. Demirtas, E., & Pechenizkiy, M. (2013, August). Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (pp. 1-8). DOI:10.1145/2502069.2502078
  12. Alqaraleh, S. (2020). Turkish Sentiment Analysis System via Ensemble Learning. Avrupa Bilim ve Teknoloji Dergisi, 122-129. DOI:10.31590/ejosat.779181
  13. Kemaloğlu, N., Küçüksille, E., & Özgünsür; M.E. (2021), Turkish Sentiment Analysis on Social Media. Sakarya University Journal of Science, 25(3), 629-638, DOI: https://doi.org/10.16984/saufenbilder.872227
  14. Ergül Aydın, Z., Kamışlı Özturk, Z., & Erzurum Ciçek, Z. İ. (2021) Turkish Sentiment Analysis for Open and Dıstance Educatıon Systems. Turkish Online Journal of Distance Education, 22(3), 124-138. DOI: 10.17718/tojde.961825
  15. Dalkilic, F., & Cam, A. (2021). Automatic Movie Rating by Using Twitter Sentiment Analysis and Monitoring Tool. Journal of Emerging Computer Technologies, 1(2), 55-60.
  16. Tokcaer, S. (2021). Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi, 16(63), 1516-1536. DOI:10.19168/jyasar.928843
  17. Ergül Aydın, Z., Kamışlı Özturk, Z., & Erzurum Ciçek, Z. İ. (2021) Turkish Sentiment Analysis for Open and Dıstance Educatıon Systems. Turkish Online Journal of Distance Education, 22(3), 124-138. DOI: 10.17718/tojde.961825