Research Article
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Tweet Classification and Sentiment Analysis on Metaverse Related Messages

Year 2021, Volume: 1 Issue: 1, 25 - 30, 31.12.2021

Özgür Ağralı Ömer Aydın

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.

Keywords

Sentiment analysis Metaverse tweet classification Twitter NLP

References

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  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058
  • 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).
  • Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
  • 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
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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
  • Alqaraleh, S. (2020). Turkish Sentiment Analysis System via Ensemble Learning. Avrupa Bilim ve Teknoloji Dergisi, 122-129. DOI:10.31590/ejosat.779181
  • 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
  • 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
  • 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.
  • Tokcaer, S. (2021). Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi, 16(63), 1516-1536. DOI:10.19168/jyasar.928843

Year 2021, Volume: 1 Issue: 1, 25 - 30, 31.12.2021

Özgür Ağralı Ömer Aydın

Abstract

References

  • 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
  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058
  • 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).
  • Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
  • 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
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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
  • Alqaraleh, S. (2020). Turkish Sentiment Analysis System via Ensemble Learning. Avrupa Bilim ve Teknoloji Dergisi, 122-129. DOI:10.31590/ejosat.779181
  • 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
  • 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
  • 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.
  • Tokcaer, S. (2021). Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi, 16(63), 1516-1536. DOI:10.19168/jyasar.928843
There are 16 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Özgür Ağralı 0000-0001-6974-011X

Ömer Aydın 0000-0002-7137-4881

Publication Date December 31, 2021
Submission Date November 5, 2021
Published in Issue Year 2021 Volume: 1 Issue: 1

Cite

APA Ağralı, Ö., & Aydın, Ö. (2021). Tweet Classification and Sentiment Analysis on Metaverse Related Messages. Journal of Metaverse, 1(1), 25-30.
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Journal of Metaverse
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Izmir Academy Association
www.izmirakademi.org