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Yıl 2020, Cilt: 3 Sayı: 3, 250 - 263, 30.12.2020

Ahmet Anıl Müngen , İrfan Aygün , Mehmet Kaya

https://doi.org/10.35377/saucis.03.03.830867
Cited By: 2

Öz

Kaynakça

  • K. Ahmed, N. El Tazi, and A. H. Hossny, “Sentiment Analysis over Social Networks: An Overview,” in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, pp. 2174–2179, 2015.
  • D. Guo and C. Chen, “Detecting Non-personal and Spam Users on Geo-tagged Twitter Network,” Trans. GIS, vol. 18, no. 3, pp. 370–384, 2014.
  • A. K. Uysal and S. Gunal, “The impact of preprocessing on text classification,” Inf. Process. Manag., vol. 50, no. 1, pp. 104–112, 2014.
  • D. Yang, D. Zhang, Z. Yu, and Z. Wang, “A sentiment-enhanced personalized location recommendation system,” in HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 119–128, 2013.
  • D. Borth, R. Ji, T. Chen, T. Breuel, and S. F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in MM 2013 - Proceedings of the 2013 ACM Multimedia Conference, pp. 223–232, 2013.
  • M. Anjaria and R. M. R. Guddeti, “A novel sentiment analysis of social networks using supervised learning,” Soc. Netw. Anal. Min., vol. 4, no. 1, pp. 1–15, 2014.
  • X. Wei, G. Xu, H. Wang, Y. He, Z. Han, and W. Wang, “Sensing Users’ Emotional Intelligence in Social Networks,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 1, pp. 103–112, 2020.
  • L. Lin, J. Li, R. Zhang, W. Yu, and C. Sun, “Opinion mining and sentiment analysis in social networks: A retweeting structure-aware approach,” in Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014, pp. 890–895, 2014.
  • F. Neri, C. Aliprandi, F. Capeci, M. Cuadros, and T. By, “Sentiment analysis on social media,” in Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp. 919–926, 2012.
  • Contratres, F. G., Alves-Souza, S. N., Filgueiras, L. V. L., & DeSouza, L. S. “Sentiment analysis of social network data for cold-start relief in recommender systems,” In Advances in Intelligent Systems and Computing, vol. 746, pp. 122–132, 2018.
  • Tang, J., Zhang, Y., Sun, J., Rao, J., Yu, W., Chen, Y., & Fong, A. C. M. “Quantitative study of individual emotional states in social networks”, IEEE Transactions on Affective Computing, 3(2), 132–144, 2012.
  • F. A. Pozzi, E. Fersini, Sentiment Analysis in Social Networks. Cambridge, M.A, USA: Morgan Kaufmann, 2017.
  • M. Baldoni, C. Baroglio, V. Patti, and P. Rena, “From tags to emotions: Ontology-driven sentiment analysis in the social Semantic Web,” in CEUR Workshop Proceedings, vol. 771, no. 1, pp. 41–54, 2011.
  • M. Kanakaraj and R. M. R. Guddeti, “NLP based sentiment analysis on Twitter data using ensemble classifiers,” in 2015 3rd International Conference on Signal Processing, Communication and Networking, ICSCN 2015, 2015.
  • H. Saif, M. Fernandez, Y. He, and H. Alani, “SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8465 LNCS, pp. 83–98, 2014.
  • A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine Learning-Based Sentiment Analysis for Twitter Accounts,” Math. Comput. Appl., vol. 23, no. 1, p. 11, 2018.
  • V. Sindhwani and P. Melville, “Document-word co-regularization for semi-supervised sentiment analysis,” in Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1025–1030, 2008.
  • E. Fersini, F. A. Pozzi, and E. Messina, “Approval network: a novel approach for sentiment analysis in social networks,” World Wide Web, vol. 20, no. 4, pp. 831–854, 2017.
  • A. A. Müngen and M. Kaya, “Influence analysis of posts in social networks by using quad-motifs,” in IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, 2017.
  • H. Alp, “Çingenelere Yönelik Nefret Söyleminin Ekşi Sözlük’te Yeniden Üretilmesi,” İlef Derg., vol. 3, no. 2, pp. 143–172, 2016.
  • B. Dogu, B. Dogu, Z. Ziraman, and D. E. Ziraman, “Web Based Authorship in the Context of User Generated Content, An Analysis of a Turkish Web Site: Eksi Sozluk,” Accessed: Oct. 13, 2020.
  • F. Akınerdem, “Yerli dizi anlatıları ve izleYici katılımı: uçurum dizisini ekşisÖzlük ve twitter’la birlikte izlemek,” Folklor/Edebiyat, 18(72), pp. 77-90, 2012.
  • A. Depoux, S. Martin, E. Karafillakis, R. Preet, A. Wilder-Smith, and H. Larson, “The pandemic of social media panic travels faster than the COVID-19 outbreak,” Journal of Travel Medicine, Oxford University Press, vol. 27, no. 3, 2020.
  • J. Gao et al., “Mental health problems and social media exposure during COVID-19 outbreak,” PLoS One, vol. 15, no. 4, 2020.
  • C. Li, L. J. Chen, X. Chen, M. Zhang, C. P. Pang, and H. Chen, “Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020,” Eurosurveillance, European Centre for Disease Prevention and Control (ECDC), vol. 25, no. 10, 2020.
  • G. Pennycook, J. McPhetres, Y. Zhang, J. G. Lu, and D. G. Rand, “Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention,” Psychol. Sci., vol. 31, no. 7, pp. 770–780, 2020.
  • L. Li et al., “Characterizing the Propagation of Situational Information in Social Media during COVID-19 Epidemic: A Case Study on Weibo,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 556–562, 2020.
  • R. Güner, İ. Hasanoğlu, and F. Aktaş, “COVID-19: Prevention and control measures in community,” TURKISH J. Med. Sci., vol. 50, no. SI-1, pp. 571–577, 2020.
  • H. Akca, “The Internet as a Participatory Medium: An Analysis of the Eksi Sozluk Website as a Public Sphere,” Theses Diss., [Online]. Available: https://scholarcommons.sc.edu/etd/304, 2010.
  • “T.C.CUMHURBAŞKANLIĞI : Cumhurbaşkanlığı Sözcüsü Kalın: ‘Korona Virüs’le mücadele sürecini, el birliğiyle rehavete ve paniğe kapılmadan atlatma kabiliyetine sahibiz.’” https://www.tccb.gov.tr/haberler/410/117021/cumhurbaskanligi-sozcusu-kalin-korona-virus-le-mucadele-surecini-el-birligiyle-rehavete-ve-panige-kapilmadan-atlatma-kabiliyetine-sahibiz. accessed Jun. 10, 2020.
  • “T.C Sağlık Bakanlığı Korona Tablosu.” https://covid19.saglik.gov.tr/ accessed May. 23, 2020.
  • J. Ding, H. Sun, X. Wang, and X. Liu, “Entity-level sentiment analysis of issue comments,” in Proceedings - International Conference on Software Engineering, pp. 7–13, 2018.
  • E. Dogan and B. Kaya, “Deep Learning Based Sentiment Analysis and Text Summarization in Social Networks,” in 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019, 2019.
  • M. A. Tocoglu and A. Alpkocak, “TREMO: A dataset for emotion analysis in Turkish,” J. Inf. Sci., vol. 44, no. 6, pp. 848–860, 2018.
  • T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 2013.
  • S. Fırat, “GitHub - selimfirat/bilkent-turkish-writings-dataset: Turkish writings dataset that promotes creativity, content, composition, grammar, spelling and punctuation.”. https://github.com/selimfirat/bilkent-turkish-writings-dataset, 2017.
  • J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” 2014.
  • R. Rana, “Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech,” [Online]. Available: http://arxiv.org/abs/1612.07778, 2016.
  • J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, pp. 4171–4186, 2018.
  • K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What Does BERT Look At? An Analysis of BERT’s Attention,” pp. 276–286, 2019.
  • “Sağlık Bakanı Koca’dan koronavirüs açıklaması: İstanbul Türkiye’nin ’Wuhan’ı oldu - Son Dakika Flaş Haberler.” https://www.cnnturk.com/turkiye/saglik-bakani-kocadan-koronavirus-aciklamasi-istanbul-turkiyenin-wuhani-oldu, accessed Oct. 13, 2020.

Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Yıl 2020, Cilt: 3 Sayı: 3, 250 - 263, 30.12.2020

Ahmet Anıl Müngen , İrfan Aygün , Mehmet Kaya

https://doi.org/10.35377/saucis.03.03.830867
Cited By: 2

Öz

Nowadays, social media and online sharing sites are frequently used to share thoughts about daily events. Thanks to the posts made by internet users on these platforms, first, quite big data is generated to interpret the agenda. More than 10,000 comments of more than 5000 users made about COVID-19 from online websites between 15 March and 15 May were collected in this study. Then, emotional analysis on these comments was carried out with BERT, GRU, LSTM and TF-IDF methods. The changes in the amount of user comments and the emotions reflected by the comments have been associated with the actual events of these dates. It has been determined which types of events affect users more. In addition, the emotional response changes of the users to the official COVID-19 statistics were measured and the peak points of the emotional changes were determined. Finally, the emotion classification methods applied were evaluated by user questionnaires and their successes were determined according to F-Measure.

Anahtar Kelimeler

COVID-19 LSTM BERT Sentiment Analysis TF-IDF

Kaynakça

  • K. Ahmed, N. El Tazi, and A. H. Hossny, “Sentiment Analysis over Social Networks: An Overview,” in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, pp. 2174–2179, 2015.
  • D. Guo and C. Chen, “Detecting Non-personal and Spam Users on Geo-tagged Twitter Network,” Trans. GIS, vol. 18, no. 3, pp. 370–384, 2014.
  • A. K. Uysal and S. Gunal, “The impact of preprocessing on text classification,” Inf. Process. Manag., vol. 50, no. 1, pp. 104–112, 2014.
  • D. Yang, D. Zhang, Z. Yu, and Z. Wang, “A sentiment-enhanced personalized location recommendation system,” in HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 119–128, 2013.
  • D. Borth, R. Ji, T. Chen, T. Breuel, and S. F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in MM 2013 - Proceedings of the 2013 ACM Multimedia Conference, pp. 223–232, 2013.
  • M. Anjaria and R. M. R. Guddeti, “A novel sentiment analysis of social networks using supervised learning,” Soc. Netw. Anal. Min., vol. 4, no. 1, pp. 1–15, 2014.
  • X. Wei, G. Xu, H. Wang, Y. He, Z. Han, and W. Wang, “Sensing Users’ Emotional Intelligence in Social Networks,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 1, pp. 103–112, 2020.
  • L. Lin, J. Li, R. Zhang, W. Yu, and C. Sun, “Opinion mining and sentiment analysis in social networks: A retweeting structure-aware approach,” in Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014, pp. 890–895, 2014.
  • F. Neri, C. Aliprandi, F. Capeci, M. Cuadros, and T. By, “Sentiment analysis on social media,” in Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp. 919–926, 2012.
  • Contratres, F. G., Alves-Souza, S. N., Filgueiras, L. V. L., & DeSouza, L. S. “Sentiment analysis of social network data for cold-start relief in recommender systems,” In Advances in Intelligent Systems and Computing, vol. 746, pp. 122–132, 2018.
  • Tang, J., Zhang, Y., Sun, J., Rao, J., Yu, W., Chen, Y., & Fong, A. C. M. “Quantitative study of individual emotional states in social networks”, IEEE Transactions on Affective Computing, 3(2), 132–144, 2012.
  • F. A. Pozzi, E. Fersini, Sentiment Analysis in Social Networks. Cambridge, M.A, USA: Morgan Kaufmann, 2017.
  • M. Baldoni, C. Baroglio, V. Patti, and P. Rena, “From tags to emotions: Ontology-driven sentiment analysis in the social Semantic Web,” in CEUR Workshop Proceedings, vol. 771, no. 1, pp. 41–54, 2011.
  • M. Kanakaraj and R. M. R. Guddeti, “NLP based sentiment analysis on Twitter data using ensemble classifiers,” in 2015 3rd International Conference on Signal Processing, Communication and Networking, ICSCN 2015, 2015.
  • H. Saif, M. Fernandez, Y. He, and H. Alani, “SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8465 LNCS, pp. 83–98, 2014.
  • A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine Learning-Based Sentiment Analysis for Twitter Accounts,” Math. Comput. Appl., vol. 23, no. 1, p. 11, 2018.
  • V. Sindhwani and P. Melville, “Document-word co-regularization for semi-supervised sentiment analysis,” in Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1025–1030, 2008.
  • E. Fersini, F. A. Pozzi, and E. Messina, “Approval network: a novel approach for sentiment analysis in social networks,” World Wide Web, vol. 20, no. 4, pp. 831–854, 2017.
  • A. A. Müngen and M. Kaya, “Influence analysis of posts in social networks by using quad-motifs,” in IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, 2017.
  • H. Alp, “Çingenelere Yönelik Nefret Söyleminin Ekşi Sözlük’te Yeniden Üretilmesi,” İlef Derg., vol. 3, no. 2, pp. 143–172, 2016.
  • B. Dogu, B. Dogu, Z. Ziraman, and D. E. Ziraman, “Web Based Authorship in the Context of User Generated Content, An Analysis of a Turkish Web Site: Eksi Sozluk,” Accessed: Oct. 13, 2020.
  • F. Akınerdem, “Yerli dizi anlatıları ve izleYici katılımı: uçurum dizisini ekşisÖzlük ve twitter’la birlikte izlemek,” Folklor/Edebiyat, 18(72), pp. 77-90, 2012.
  • A. Depoux, S. Martin, E. Karafillakis, R. Preet, A. Wilder-Smith, and H. Larson, “The pandemic of social media panic travels faster than the COVID-19 outbreak,” Journal of Travel Medicine, Oxford University Press, vol. 27, no. 3, 2020.
  • J. Gao et al., “Mental health problems and social media exposure during COVID-19 outbreak,” PLoS One, vol. 15, no. 4, 2020.
  • C. Li, L. J. Chen, X. Chen, M. Zhang, C. P. Pang, and H. Chen, “Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020,” Eurosurveillance, European Centre for Disease Prevention and Control (ECDC), vol. 25, no. 10, 2020.
  • G. Pennycook, J. McPhetres, Y. Zhang, J. G. Lu, and D. G. Rand, “Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention,” Psychol. Sci., vol. 31, no. 7, pp. 770–780, 2020.
  • L. Li et al., “Characterizing the Propagation of Situational Information in Social Media during COVID-19 Epidemic: A Case Study on Weibo,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 556–562, 2020.
  • R. Güner, İ. Hasanoğlu, and F. Aktaş, “COVID-19: Prevention and control measures in community,” TURKISH J. Med. Sci., vol. 50, no. SI-1, pp. 571–577, 2020.
  • H. Akca, “The Internet as a Participatory Medium: An Analysis of the Eksi Sozluk Website as a Public Sphere,” Theses Diss., [Online]. Available: https://scholarcommons.sc.edu/etd/304, 2010.
  • “T.C.CUMHURBAŞKANLIĞI : Cumhurbaşkanlığı Sözcüsü Kalın: ‘Korona Virüs’le mücadele sürecini, el birliğiyle rehavete ve paniğe kapılmadan atlatma kabiliyetine sahibiz.’” https://www.tccb.gov.tr/haberler/410/117021/cumhurbaskanligi-sozcusu-kalin-korona-virus-le-mucadele-surecini-el-birligiyle-rehavete-ve-panige-kapilmadan-atlatma-kabiliyetine-sahibiz. accessed Jun. 10, 2020.
  • “T.C Sağlık Bakanlığı Korona Tablosu.” https://covid19.saglik.gov.tr/ accessed May. 23, 2020.
  • J. Ding, H. Sun, X. Wang, and X. Liu, “Entity-level sentiment analysis of issue comments,” in Proceedings - International Conference on Software Engineering, pp. 7–13, 2018.
  • E. Dogan and B. Kaya, “Deep Learning Based Sentiment Analysis and Text Summarization in Social Networks,” in 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019, 2019.
  • M. A. Tocoglu and A. Alpkocak, “TREMO: A dataset for emotion analysis in Turkish,” J. Inf. Sci., vol. 44, no. 6, pp. 848–860, 2018.
  • T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 2013.
  • S. Fırat, “GitHub - selimfirat/bilkent-turkish-writings-dataset: Turkish writings dataset that promotes creativity, content, composition, grammar, spelling and punctuation.”. https://github.com/selimfirat/bilkent-turkish-writings-dataset, 2017.
  • J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” 2014.
  • R. Rana, “Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech,” [Online]. Available: http://arxiv.org/abs/1612.07778, 2016.
  • J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, pp. 4171–4186, 2018.
  • K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What Does BERT Look At? An Analysis of BERT’s Attention,” pp. 276–286, 2019.
  • “Sağlık Bakanı Koca’dan koronavirüs açıklaması: İstanbul Türkiye’nin ’Wuhan’ı oldu - Son Dakika Flaş Haberler.” https://www.cnnturk.com/turkiye/saglik-bakani-kocadan-koronavirus-aciklamasi-istanbul-turkiyenin-wuhani-oldu, accessed Oct. 13, 2020.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Ahmet Anıl Müngen 0000-0002-5691-6507

İrfan Aygün 0000-0003-4889-4704

Mehmet Kaya 0000-0003-2995-8282

Yayımlanma Tarihi 30 Aralık 2020
Gönderilme Tarihi 24 Kasım 2020
Kabul Tarihi 24 Kasım 2020
Yayımlandığı Sayı Yıl 2020Cilt: 3 Sayı: 3

Kaynak Göster

IEEE A. A. Müngen, İ. Aygün, ve M. Kaya, “Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process”, SAUCIS, c. 3, sy. 3, ss. 250–263, 2020, doi: 10.35377/saucis.03.03.830867.

Cited By

Sentiments towards Emergency Remote Teaching on Twitter: A Longitudinal Comparative Sentiment Analysis
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Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images
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https://doi.org/10.35377/saucis.04.01.879735
 Kapak Resmi İndir

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