Research Article
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INVESTIGATION OF TEXT AND IMAGE BASED SEMANTIC INFERENCE METHODS IN SOCIAL NETWORKS

Year 2020, Volume: 28 Issue: 3, 283 - 288, 31.12.2020
https://doi.org/10.31796/ogummf.757641

Abstract

Due to the fact that social networks are becoming more and more popular, many users are continuously active social networking users. Active users are constantly sharing what they see and think. Social network users follow the people they are interested in, comment on their messages and forward them to other people. In this way, there is a huge amount of data generated by users on social networks. Besides; in addition to textual sharing, image and video sharing are also available through mobile phones with a camera. In this study, text and image-based semantic inference methods in social networks were investigated. The advantages of different semantic inference methods are emphasized. Finally, information was given on what can be done in this area in the future.

References

  • Akaichi, J., (2014). A Medical Social Network for Physicians’ Annotations Posting and Summarization. Social Network Analysis and Mining, 4, 1-8. doi: 10.1007/s13278-014-0225-1.
  • Al-Abad, A.M., Al-Sahail, B.A., Al-Henaki, B.A., Al-Zaid, D.A., Al-Andas, G.A., Al-Mazroo, R.M. and Al-Ogaiel, R.M., (2009). A Semantic Social Network Service for Educating Saudi Breast Cancer Patients. Ninth IEEE International Conference on Advanced Learning Technologies, July 2009, Riga, Latvia, p.81-82.
  • Apaydin, A., Celik, D. and Elci, A., (2010). Semantic Image Retrieval Model for Sharing Experiences in Social Networks. IEEE 34th Annual Computer Software and Applications Conference Workshops, July 2010, Seoul, South Korea, p.1-6.
  • Ayadi, M.G., Bouslimi, R. and Akaichi, A., (2016). Medical Image Retrieval Scheme Through A Medical Social Network. Network Modeling Analysis in Health Informatics and Bioinformatics, 5, 1-20. doi: 10.1007/s13721-016-0130-9.
  • Barnes, J.A., (1969). Graph Theory and Social Networks: A Technical Comment on Connectedness and Connectivity. Sociology, 3(2), 215-232. doi: 10.1177/003803856900300205.
  • Bontcheva, K. and Rout, D., (2014). Making Sense of Social Media Streams Through Semantics: A Survey. Semantic Web, 5(5), 373-403. doi: 10.3233/SW-130110.
  • Bouslimi, R., Ayadi, M.G. and Akaichi, J., (2017). Semantic Medical Image Retrieval in A Medical Social Network. Social Network Analysis and Mining, 7(2), 1-11. doi: 10.1007/s13278-016-0420-3.
  • Demirci, M.S. ve Sagiroglu, S., (2017). Sosyal Ağ Verilerinin Kullanım Alanları Üzerine Kapsamlı Bir İnceleme. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 1-21.
  • Elahi, N., Karlsen, R. and Younas, W., (2010). Semantic Image Annotation with Social Context. International Conference for Internet Technology and Secured Transactions, November 2010, London, UK, p.1-7.
  • Federico, L., Néstor, D. and Oscar, C., (2012). SMITag: A Social Network for Semantic Annotation of Medical Images. XXXVIII Conferencia Latinoamericana En Informatica (CLEI), October 2012, Medellin, Colombia, p. 1-7.
  • Kou, F., Du, J., He, Y. and Ye, L., (2016). Social Network Search based on Semantic Analysis and Learning. CAAI Transactions on Intelligence Technology, 1(4), 293-302. doi: 10.1016/j.trit.2016.12.001.
  • Krishnamurthy, M., Mahmood, K. and Marcinek, P., (2016). A Hybrid Statistical and Semantic Model for Identification of Mental Health and Behavioral Disorders using Social Network Analysis. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 2016, San Francisco, CA, USA, p.1019-1026.
  • Lu, Z., Wang, L. and Wen, J.R., (2014). Direct Semantic Analysis for Social Image Classification. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 2014, Québec, Canada, p.1258-1264.
  • Merler, M., Cao, L. and Smith, J.R., (2015). You Are What You Tweet…Pic! Gender Prediction based on Semantic Analysis of Social Media Images. IEEE International Conference on Multimedia and Expo (ICME), July 2015, Turin, Italy, p.1-6.
  • Meüller, H., Clough, P., Deselaers, T. and Caputo, B., (2010). ImageCLEF: Experimental Evaluation in Visual Information Retrieval. Springer, Berlin, 32, 544p.
  • Qian, X., Hua, X.S., Tang, Y.Y. and Mei, T., (2014). Social Image Tagging With Diverse Semantics. IEEE Transactions on Cybernetics, 44(12), 2493-2508. doi: 10.1109/TCYB.2014.2309593.
  • Thovex, C. and Trichet, F., (2013). Opinion Mining and Semantic Analysis of Touristic Social Networks. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 2013, Niagara Falls, ON, Canada, p.1155-1160.
  • Wang, J. and Li, G., (2017). A Multi-modal Hashing Learning Framework for Automatic Image Annotation. IEEE Second International Conference on Data Science in Cyberspace (DSC), June 2017, Shenzhen, China, p.14-21.
  • Wang, Y., Wang, S., Tang, J., Liu, H. and Li, B., (2015). Unsupervised Sentiment Analysis for Social Media Images. IJCAI International Joint Conference on Artificial Intelligence, July 2015, Buenos Aires, Argentina, p.2378-2379.
  • Wickramaarachchi, W.U. and Kariapper, R.K.A.R., (2017). An Approach to get Overall Emotion from Comment Text towards A Certain Image Uploaded to Social Network using Latent Semantic Analysis. 2nd International Conference on Image, Vision and Computing (ICIVC), June 2017, Chengdu, China, p.788-792.
  • Yu, Y., Lin, H., Meng, J. and Zhao, Z., (2016). Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks, Algorithms, 9(2), 1-11. doi: 10.3390/a9020041.

SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ

Year 2020, Volume: 28 Issue: 3, 283 - 288, 31.12.2020
https://doi.org/10.31796/ogummf.757641

Abstract

Sosyal ağların gün geçtikçe daha popüler hale gelmesi sebebiyle, çok sayıda kullanıcı sürekli olarak aktif sosyal ağ kullanıcısı durumundadır. Aktif kullanıcılar sürekli olarak gördüklerini ve düşündüklerini paylaşmaktadır. Sosyal ağ kullanıcıları ilgilendikleri kişileri takip etmekte, mesajlarına yorum yapmakta ve diğer kişilere iletmektedir. Bu şekilde, sosyal ağlarda kullanıcılar tarafından üretilen çok büyük miktarda veri bulunmaktadır. Bunun yanında; kameralı cep telefonları sayesinde metinsel paylaşımlar yanında görüntü ve video paylaşımları da yapılmaktadır. Bu çalışmada, sosyal ağlarda metin ve görüntü tabanlı anlamsal çıkarım yöntemleri incelenmiştir. Farklı anlamsal çıkarım yöntemlerinin, kullanıldığı alanlara göre avantajları vurgulanmıştır. Son olarak da, bu alanda gelecekte ne gibi çalışmalar yapılabileceğine dair bilgiler verilmiştir.

References

  • Akaichi, J., (2014). A Medical Social Network for Physicians’ Annotations Posting and Summarization. Social Network Analysis and Mining, 4, 1-8. doi: 10.1007/s13278-014-0225-1.
  • Al-Abad, A.M., Al-Sahail, B.A., Al-Henaki, B.A., Al-Zaid, D.A., Al-Andas, G.A., Al-Mazroo, R.M. and Al-Ogaiel, R.M., (2009). A Semantic Social Network Service for Educating Saudi Breast Cancer Patients. Ninth IEEE International Conference on Advanced Learning Technologies, July 2009, Riga, Latvia, p.81-82.
  • Apaydin, A., Celik, D. and Elci, A., (2010). Semantic Image Retrieval Model for Sharing Experiences in Social Networks. IEEE 34th Annual Computer Software and Applications Conference Workshops, July 2010, Seoul, South Korea, p.1-6.
  • Ayadi, M.G., Bouslimi, R. and Akaichi, A., (2016). Medical Image Retrieval Scheme Through A Medical Social Network. Network Modeling Analysis in Health Informatics and Bioinformatics, 5, 1-20. doi: 10.1007/s13721-016-0130-9.
  • Barnes, J.A., (1969). Graph Theory and Social Networks: A Technical Comment on Connectedness and Connectivity. Sociology, 3(2), 215-232. doi: 10.1177/003803856900300205.
  • Bontcheva, K. and Rout, D., (2014). Making Sense of Social Media Streams Through Semantics: A Survey. Semantic Web, 5(5), 373-403. doi: 10.3233/SW-130110.
  • Bouslimi, R., Ayadi, M.G. and Akaichi, J., (2017). Semantic Medical Image Retrieval in A Medical Social Network. Social Network Analysis and Mining, 7(2), 1-11. doi: 10.1007/s13278-016-0420-3.
  • Demirci, M.S. ve Sagiroglu, S., (2017). Sosyal Ağ Verilerinin Kullanım Alanları Üzerine Kapsamlı Bir İnceleme. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 1-21.
  • Elahi, N., Karlsen, R. and Younas, W., (2010). Semantic Image Annotation with Social Context. International Conference for Internet Technology and Secured Transactions, November 2010, London, UK, p.1-7.
  • Federico, L., Néstor, D. and Oscar, C., (2012). SMITag: A Social Network for Semantic Annotation of Medical Images. XXXVIII Conferencia Latinoamericana En Informatica (CLEI), October 2012, Medellin, Colombia, p. 1-7.
  • Kou, F., Du, J., He, Y. and Ye, L., (2016). Social Network Search based on Semantic Analysis and Learning. CAAI Transactions on Intelligence Technology, 1(4), 293-302. doi: 10.1016/j.trit.2016.12.001.
  • Krishnamurthy, M., Mahmood, K. and Marcinek, P., (2016). A Hybrid Statistical and Semantic Model for Identification of Mental Health and Behavioral Disorders using Social Network Analysis. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 2016, San Francisco, CA, USA, p.1019-1026.
  • Lu, Z., Wang, L. and Wen, J.R., (2014). Direct Semantic Analysis for Social Image Classification. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 2014, Québec, Canada, p.1258-1264.
  • Merler, M., Cao, L. and Smith, J.R., (2015). You Are What You Tweet…Pic! Gender Prediction based on Semantic Analysis of Social Media Images. IEEE International Conference on Multimedia and Expo (ICME), July 2015, Turin, Italy, p.1-6.
  • Meüller, H., Clough, P., Deselaers, T. and Caputo, B., (2010). ImageCLEF: Experimental Evaluation in Visual Information Retrieval. Springer, Berlin, 32, 544p.
  • Qian, X., Hua, X.S., Tang, Y.Y. and Mei, T., (2014). Social Image Tagging With Diverse Semantics. IEEE Transactions on Cybernetics, 44(12), 2493-2508. doi: 10.1109/TCYB.2014.2309593.
  • Thovex, C. and Trichet, F., (2013). Opinion Mining and Semantic Analysis of Touristic Social Networks. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 2013, Niagara Falls, ON, Canada, p.1155-1160.
  • Wang, J. and Li, G., (2017). A Multi-modal Hashing Learning Framework for Automatic Image Annotation. IEEE Second International Conference on Data Science in Cyberspace (DSC), June 2017, Shenzhen, China, p.14-21.
  • Wang, Y., Wang, S., Tang, J., Liu, H. and Li, B., (2015). Unsupervised Sentiment Analysis for Social Media Images. IJCAI International Joint Conference on Artificial Intelligence, July 2015, Buenos Aires, Argentina, p.2378-2379.
  • Wickramaarachchi, W.U. and Kariapper, R.K.A.R., (2017). An Approach to get Overall Emotion from Comment Text towards A Certain Image Uploaded to Social Network using Latent Semantic Analysis. 2nd International Conference on Image, Vision and Computing (ICIVC), June 2017, Chengdu, China, p.788-792.
  • Yu, Y., Lin, H., Meng, J. and Zhao, Z., (2016). Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks, Algorithms, 9(2), 1-11. doi: 10.3390/a9020041.
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Sümeyye Bayrakdar 0000-0002-8148-1090

İbrahim Yücedağ 0000-0003-2975-7392

Publication Date December 31, 2020
Acceptance Date November 4, 2020
Published in Issue Year 2020 Volume: 28 Issue: 3

Cite

APA Bayrakdar, S., & Yücedağ, İ. (2020). SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 28(3), 283-288. https://doi.org/10.31796/ogummf.757641
AMA Bayrakdar S, Yücedağ İ. SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ. ESOGÜ Müh Mim Fak Derg. December 2020;28(3):283-288. doi:10.31796/ogummf.757641
Chicago Bayrakdar, Sümeyye, and İbrahim Yücedağ. “SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 28, no. 3 (December 2020): 283-88. https://doi.org/10.31796/ogummf.757641.
EndNote Bayrakdar S, Yücedağ İ (December 1, 2020) SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28 3 283–288.
IEEE S. Bayrakdar and İ. Yücedağ, “SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ”, ESOGÜ Müh Mim Fak Derg, vol. 28, no. 3, pp. 283–288, 2020, doi: 10.31796/ogummf.757641.
ISNAD Bayrakdar, Sümeyye - Yücedağ, İbrahim. “SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28/3 (December 2020), 283-288. https://doi.org/10.31796/ogummf.757641.
JAMA Bayrakdar S, Yücedağ İ. SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ. ESOGÜ Müh Mim Fak Derg. 2020;28:283–288.
MLA Bayrakdar, Sümeyye and İbrahim Yücedağ. “SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 28, no. 3, 2020, pp. 283-8, doi:10.31796/ogummf.757641.
Vancouver Bayrakdar S, Yücedağ İ. SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ. ESOGÜ Müh Mim Fak Derg. 2020;28(3):283-8.

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